This paper presents a particle-based visualization approach for finite-time analysis of the connectivity of fluid portions in multiphase flow, i.e., the evolution of the droplets in volume of fluid simulations. We address the Lagrangian inconsistency between the interpolated flow field and the interpolated volume of fluid field by a correction approach, and complement that with an uncertainty measure that provides an estimate of the involved inconsistency. We demonstrate the utility and versatility of our approach using different multiphase flow simulations, exemplify its application in physics-based assessment of droplet formation processes, and discuss its limitations and benefits.

Despite the availability of hotel booking platforms with customer reviews, selecting the best hotel or accommodation can still be difficult. This is partly because it is hard to directly contrast multiple shortlisted hotels. We propose a new visual interface, called ViSCitR, that goes beyond existing solutions in visually comparing ratings and personalizing the evaluation based on individual priorities. Targeted at a broad audience, it integrates several dimensions of comparison into a highly interactive document. The document contrasts the hotels visually and textually from different perspectives, including (i) a geographic perspective with points of interest, (ii) a comparison of ratings across different categories, (iii) rating changes over time, and (iv) summaries of the most noted positive and negative points from the review texts. The interface is accompanied by a sidebar for personalization, which supports selecting relevant points of interest and setting priorities. Changing these adapts the visual comparison across all interface sections to match the user’s preferences. A qualitative evaluation with potential hotel customers showed that the personalized visual comparison is valuable for all users, while the advanced features of the interface are leveraged to different extents.

Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one annotated frame per action. However, it often suffers from poor performance due to the lack of precise boundary annotations. To address this issue, we propose a visual analysis method that aligns similar actions and then propagates a few user-provided annotations (e.g., boundaries, category labels) to similar actions via the generated alignments. Our method models the alignment between actions as a heaviest path problem and the annotation propagation as a quadratic optimization problem. As the automatically generated alignments may not accurately match the associated actions and could produce inaccurate localization results, we develop a storyline visualization to explain the localization results of actions and their alignments. This visualization facilitates users in correcting wrong localization results and misalignments. The corrections are then used to improve the localization results of other actions. The effectiveness of our method in improving localization performance is demonstrated through quantitative evaluation and a case study.

We developed a new approach comprised of different visualizations for the comparative spatio-temporal analysis of displacement processes in porous media. We aim to analyze and compare ensemble datasets from experiments to gain insight into the influence of different parameters on fluid flow. To capture the displacement of a defending fluid by an invading fluid, we first condense an input image series to a single time map. From this map, we generate a spatio-temporal flow graph covering the whole process. This graph is further simplified to only reflect topological changes in the movement of the invading fluid. Our interactive tools allow the visual analysis of these processes by visualizing the graph structure and the context of the experimental setup, as well as by providing charts for multiple metrics. We apply our approach to analyze and compare ensemble datasets jointly with domain experts, where we vary either fluid properties or the solid structure of the porous medium. We finally report the generated insights from the domain experts and discuss our contribution's advantages, generality, and limitations.

The visualization of results while the simulation is running is increasingly common in extreme scale computing environments. We present a novel approach for in situ generation of image databases to achieve cost savings on supercomputers. Our approach, a hybrid between traditional inline and in transit techniques, dynamically distributes visualization tasks between simulation nodes and visualization nodes, using probing as a basis to estimate rendering cost. Our hybrid design differs from previous works in that it creates opportunities to minimize idle time from four fundamental types of inefficiency: variability, limited scalability, overhead, and rightsizing. We demonstrate our results by comparing our method against both inline and in transit methods for a variety of configurations, including two simulation codes and a scaling study that goes above 19 K cores. Our findings show that our approach is superior in many configurations. As in situ visualization becomes increasingly ubiquitous, we believe our technique could lead to significant amounts of reclaimed cycles on supercomputers.

We present a new hierarchical multifocus representation- and interaction-technique for the analysis of text annotations. The comparative analysis of annotation data on multiple, distant passages (focus regions) of a long source text raises several scalability problems. In particular, one soon can be confronted with many nested foci on text ranges with sizes of different orders of magnitude. Our solution suggests to delegate the detailed data representation to other views and to concentrate in the presented overview on the organization of the focus regions. The approach consists of three parts: a collection of strips where the annotations are represented in a condensed form and where sibling- and child-foci are manipulated, a map of the resulting tree of foci for direct access, and a work bench that allows to compare deliberate nodes of the tree on a flat layer. We discuss our approach by comparing it with other state-of-the-art multifocus techniques and describe two use cases that deal with relational annotation and with the analysis of annotations on hierarchical text structures respectively.

Test methods that can keep up with the ongoing increase in complexity of semiconductor products and their underlying technologies are an essential prerequisite for maintaining quality and safety of our daily lives and for continued success of our economies and societies. There is a huge potential how test methods can benefit from recent breakthroughs in domains such as artificial intelligence, data analytics, virtual/augmented reality, and security. The Graduate School on “Intelligent Methods for Semiconductor Test and Reliability” (GS-IMTR) at the University of Stuttgart is a large-scale, radically interdisciplinary effort to address the scientific-technological challenges in this domain. It is funded by Advantest, one of the world leaders in automatic test equipment. In this paper, we describe the overall philosophy of the Graduate School and the specific scientific questions targeted by its ten projects.

Data visualization relies on efficient rendering to allow users to interactively explore and understand their data. However, achieving interactive frame rates is often challenging, especially for high-resolution displays or large datasets. In computer graphics, several methods temporally reconstruct full-resolution images from multiple consecutive lower-resolution frames. Besides providing temporal image stability, they amortize the rendering costs over multiple frames and thus improve the minimum frame rate. We present a method that adopts this idea to accelerate 2D information visualization, without requiring any changes to the rendering itself. By exploiting properties of orthographic projection, our method significantly improves rendering performance while minimizing the loss of image quality during camera manipulation. For static scenes, it quickly converges to the full-resolution image. We discuss the characteristics of our method concerning rendering performance and image quality and the corresponding trade-offs. Finally, we present extensive rendering benchmarks to examine real-world performance for examples of parallel coordinates and scatterplot matrix visualizations, and discuss appropriate application scenarios and contraindications for usage.

When visualizations are used to augment text, geographical information on a map, or on mobile devices, there often is not enough space to show complex data with approaches like juxtaposed visualizations or coordinated views. To alleviate this issue, we propose the use of animated transitions between several small-scale visualizations. We discuss design considerations for animated transitions in small-scale visualizations. We further present the results of a study on the effectiveness of those animated transitions in conveying information and attribute relations, and the mental load of following the animated transitions.

Abstract We developed a new visualization approach to gain a better understanding of the displacement of one fluid phase by another in porous media. This is based on a recent experimental parameter study with varying capillary numbers and viscosity ratios. We analyse the temporal evolution of characteristic values in this two‐phase flow scenario and discuss how to directly compare experiments across different temporal scales. To enable spatio‐temporal analysis, we introduce a new abstract visual representation showing which paths through the porous medium were occupied and for how long. These transport networks allow to assess the impact of different acting forces and they are designed to yield expressive comparability and linking to the experimental parameter space both supported by additional visual cues. This joint work of porous media experts and visualization researchers yields new insights regarding two‐phase flow on the microscale, and our visualization approach contributes towards the overarching goal of the domain scientists to characterize porous media flow based on capillary numbers and viscosity ratios.

Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.

We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.

Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the suitability of the algorithm for larger values of k depending on the size of the collection. Optimizations targeted at the Euclidean k-Means algorithm largely do not apply because the cosine distance is not a metric. We therefore propose an efficient indexing structure to improve the scalability of Spherical k-Means with respect to k. Our approach exploits the sparsity of the input vectors and the convergence behavior of k-Means to reduce the number of comparisons on each iteration significantly.

Keyphrase extraction methods can provide insights into large collections of documents such as social media posts. Existing methods, however, are less suited for the real-time analysis of streaming data, because they are computationally too expensive or require restrictive constraints regarding the structure of keyphrases. We propose an efficient approach to extract keyphrases from large document collections and show that the method also performs competitively on individual documents.

Abstract When studying protein‐ligand interactions, many different factors can influence the behaviour of the protein as well as the ligands. Molecular visualisation tools typically concentrate on the movement of single ligand molecules; however, viewing only one molecule can merely provide a hint of the overall behaviour of the system. To tackle this issue, we do not focus on the visualisation of the local actions of individual ligand molecules but on the influence of a protein and their overall movement. Since the simulations required to study these problems can have millions of time steps, our presented system decouples visualisation and data preprocessing: our preprocessing pipeline aggregates the movement of ligand molecules relative to a receptor protein. For data analysis, we present a web‐based visualisation application that combines multiple linked 2D and 3D views that display the previously calculated data The central view, a novel enhanced sequence diagram that shows the calculated values, is linked to a traditional surface visualisation of the protein. This results in an interactive visualisation that is independent of the size of the underlying data, since the memory footprint of the aggregated data for visualisation is constant and very low, even if the raw input consisted of several terabytes.

Collaborative exploration of scientific data sets across large high-resolution displays requires both high visual detail as well as low-latency transfer of image data (oftentimes inducing the need to trade one for the other). In this work, we present a system that dynamically adapts the encoding quality in such systems in a way that reduces the required bandwidth without impacting the details perceived by one or more observers. Humans perceive sharp, colourful details, in the small foveal region around the centre of the field of view, while information in the periphery is perceived blurred and colourless. We account for this by tracking the gaze of observers, and respectively adapting the quality parameter of each macroblock used by the H.264 encoder, considering the so-called visual acuity fall-off. This allows to substantially reduce the required bandwidth with barely noticeable changes in visual quality, which is crucial for collaborative analysis across display walls at different locations. We demonstrate the reduced overall required bandwidth and the high quality inside the foveated regions using particle rendering and parallel coordinates.

It is difficult to explore large text collections if no or little information is available on the contained documents. Hence, starting analytic tasks on such corpora is challenging for many stakeholders from various domains. As a remedy, recent visualization research suggests to use visual spatializations of representative text documents or tags to explore text collections. With PyramidTags, we introduce a novel approach for summarizing large text collections visually. In contrast to previous work, PyramidTags in particular aims at creating an improved representation that incorporates both temporal evolution and semantic relationship of visualized tags within the summarized document collection. As a result, it equips analysts with a visual starting point for interactive exploration to not only get an overview of the main terms and phrases of the corpus, but also to grasp important ideas and stories. Analysts can hover and select multiple tags to explore relationships and retrieve the most relevant documents. In this work, we apply PyramidTags to hundreds of thousands of web-crawled news reports. Our benchmarks suggest that PyramidTags creates time- and context-aware layouts, while preserving the inherent word order of important pairs.

Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.

Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.

We present a machine learning-based approach for detecting and visualizing complex behavior in spatiotemporal volumes. For this, we train models to predict future data values at a given position based on the past values in its neighborhood, capturing common temporal behavior in the data. We then evaluate the model's prediction on the same data. High prediction error means that the local behavior was too complex, unique or uncertain to be accurately captured during training, indicating spatiotemporal regions with interesting behavior. By training several models of varying capacity, we are able to detect spatiotemporal regions of various complexities. We aggregate the obtained prediction errors into a time series or spatial volumes and visualize them together to highlight regions of unpredictable behavior and how they differ between the models. We demonstrate two further volumetric applications: adaptive timestep selection and analysis of ensemble dissimilarity. We apply our technique to datasets from multiple application domains and demonstrate that we are able to produce meaningful results while making minimal assumptions about the underlying data.

Due to their success in many application areas, deep learning models have found wide adoption for many problems. However, their black-box nature makes it hard to trust their decisions and to evaluate their line of reasoning. In the field of cybersecurity, this lack of trust and understanding poses a significant challenge for the utilization of deep learning models. Thus, we present a visual analytics system that provides designers of deep learning models for the classification of domain generation algorithms with understandable interpretations of their model. We cluster the activations of the model's nodes and leverage decision trees to explain these clusters. In combination with a 2D projection, the user can explore how the model views the data at different layers. In a preliminary evaluation of our system, we show how it can be employed to better understand misclassifications, identify potential biases and reason about the role different layers in a model may play.

Magic lens based focus+context techniques are powerful means for exploring document spatializations. Typically, they only offer additional summarized or abstracted views on focused documents. As a consequence, users might miss important information that is either not shown in aggregated form or that never happens to get focused. In this work, we present the design process and user study results for improving a magic lens based document exploration approach with exemplary visual quality cues to guide users in steering the exploration and support them in interpreting the summarization results. We contribute a thorough analysis of potential sources of information loss involved in these techniques, which include the visual spatialization of text documents, user-steered exploration, and the visual summarization. With lessons learned from previous research, we highlight the various ways those information losses could hamper the exploration. Furthermore, we formally define measures for the aforementioned different types of information losses and bias. Finally, we present the visual cues to depict these quality measures that are seamlessly integrated into the exploration approach. These visual cues guide users during the exploration and reduce the risk of misinterpretation and accelerate insight generation. We conclude with the results of a controlled user study and discuss the benefits and challenges of integrating quality guidance in exploration techniques.

As our field matures, evaluation of visualization techniques has extended from reporting runtime performance to studying user behavior. Consequently, many methodologies and best practices for user studies have evolved. While maintaining interactivity continues to be crucial for the exploration of large data sets, no similar methodological foundation for evaluating runtime performance has been developed. Our analysis of 50 recent visualization papers on new or improved techniques for rendering volumes or particles indicates that only a very limited set of parameters like different data sets, camera paths, viewport sizes, and GPUs are investigated, which make comparison with other techniques or generalization to other parameter ranges at least questionable. To derive a deeper understanding of qualitative runtime behavior and quantitative parameter dependencies, we developed a framework for the most exhaustive performance evaluation of volume and particle visualization techniques that we are aware of, including millions of measurements on ten different GPUs. This paper reports on our insights from statistical analysis of this data, discussing independent and linear parameter behavior and non-obvious effects. We give recommendations for best practices when evaluating runtime performance of scientific visualization applications, which can serve as a starting point for more elaborate models of performance quantification.

The analysis of subtle deviations between different versions of historical prints has been a long-standing challenge in art history research. So far, this challenge has required extensive domain knowledge, fine-tuned expert perception, and time-consuming manual labor. In this paper we introduce an explorative visual approach to facilitate fast and accurate support for the task of comparing differences between prints such as engravings and woodcuts. To this end, we have developed a customized algorithm that detects similar stroke-patterns in prints and matches them in order to allow visual alignment and automated deviation highlighting. Our visual analytics system enables art history researchers to quickly detect, document, and categorize qualitative and quantitative discrepancies, and to analyze these discrepancies using comprehensive interactions. To evaluate our approach, we conducted a user study involving both experts on historical prints and laypeople. Using our new interactive technique, our subjects found about 20 percent more differences compared to regular image viewing software as well as "paper-based" comparison. Moreover, the laypeople found the same differences as the experts when they used our system, which was not the case for conventional methods. Informal feedback showed that both laypeople and experts strongly preferred employing our system to working with conventional methods.

Magic lens based focus+context techniques are powerful means for exploring document spatializations. Typically, they only offer additional summarized or abstracted views on focused documents. As a consequence, users might miss important information that is either not shown in aggregated form or that never happens to get focused. In this work, we present the design process and user study results for improving a magic lens based document exploration approach with exemplary visual quality cues to guide users in steering the exploration and support them in interpreting the summarization results. We contribute a thorough analysis of potential sources of information loss involved in these techniques, which include the visual spatialization of text documents, user-steered exploration, and the visual summarization. With lessons learned from previous research, we highlight the various ways those information losses could hamper the exploration. Furthermore, we formally define measures for the aforementioned different types of information losses and bias. Finally, we present the visual cues to depict these quality measures that are seamlessly integrated into the exploration approach. These visual cues guide users during the exploration and reduce the risk of misinterpretation and accelerate insight generation. We conclude with the results of a controlled user study and discuss the benefits and challenges of integrating quality guidance in exploration techniques.

We present a method for the spatio-temporal analysis of gaze data from multiple participants in the context of a video stimulus. For such data, an overview of the recorded patterns is important to identify common viewing behavior (such as attentional synchrony) and outliers. We adopt the approach of space-time cube visualization, which extends the spatial dimensions of the stimulus by time as the third dimension. Previous work mainly handled eye tracking data in the space-time cube as point cloud, providing no information about the stimulus context. This paper presents a novel visualization technique that combines gaze data, a dynamic stimulus, and optical flow with volume rendering to derive an overview of the data with contextual information. With specifically designed transfer functions, we emphasize different data aspects, making the visualization suitable for explorative analysis and for illustrative support of statistical findings alike.

The increasing availability of digital multimedia content has led to the need of new approaches for the analysis of large databases containing video and associated data, for example, subtitles. Visualization provides valuable insights of such dataset, complementing approaches solely based on techniques for knowledge discovery in databases and information retrieval. Hence, visual analytics, combining automatic processing with interactive data visualization, has proven to be an effective means to explore and interpret such data. The analysis of news corpora represents a typical task for such a scenario. Domain experts such as journalists and social science scholars require an overview of important topics, the temporal coherence of events, and they should be able to compare different topics. We present a visual analytics approach that aims to support these tasks with automatic video preprocessing, topic extraction, clustering, and dimensionality reduction. Coordinated linked views support the flexible inspection of the dataset and the processed results. We further discuss the application of our approach in a usage scenario, inspecting the dataset of a daily news broadcast of the year 2015.

The advent of modern and affordable augmented reality head sets like Microsoft HoloLens has sparked new interest in using virtual and augmented reality technology in the analysis of molecular data. For all visualisation in immersive, mixed-reality scenarios, a sufficiently high rendering speed is an important factor, which leads to the issue of limited processing power available on fully untethered devices facing the situation of handling computationally expensive visualisations. Recent research shows that the space-filling model of even small data sets from the Protein Data Bank (PDB) cannot be rendered at desirable frame rates on the HoloLens. In this work, we report on how to improve the rendering speed of atom-based visualisation of proteins and how the rendering of more abstract representations of the molecules compares against it. We complement our findings with in-depth GPU and CPU performance numbers.

Abstract The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large‐scale and up‐to‐date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up‐to‐date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal‐and‐task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back‐end and an interactive visual analytics interface. The monitoring component uses intelligent pre‐aggregation to allow dynamic queries in near real‐time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research.

Two-dimensional height fields are the most common data structure used for storing and rendering of terrain in offline rendering and especially real-time computer graphics. By its very nature, a height field cannot store terrain structures with multiple vertical layers such as overhanging cliffs, caves, or arches. This restriction does not apply to volumetric data structures. However, the workflow of manual modelling and editing of volumetric terrain usually is tedious and very time-consuming. Therefore, we propose to use three-dimensional curve-based primitives to efficiently model prominent, large-scale terrain features. We present a technique for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while retaining the full artistic freedom of volumetric terrains. Feature Curves also contain material information that can be complemented with local details by using per-face texture mapping. All stages of our method are GPU-accelerated using compute shaders to ensure interactive editing of terrain. Please note that this paper is an extended version of our previously published work [1] .

We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality.

We present a visualization approach for the analysis of CO2 bubble-induced attenuation in porous rock formations. As a basis for this, we introduce customized techniques to extract CO2 bubbles and their surrounding porous structure from X-ray computed tomography data (XCT) measurements. To understand how the structure of porous media influences the occurrence and the shape of formed bubbles, we automatically classify and relate them in terms of morphology and geometric features, and further directly support searching for promising porous structures. To allow for the meaningful direct visual comparison of bubbles and their structures, we propose a customized registration technique considering the bubble shape as well as its points of contact with the porous media surface. With our quantitative extraction of geometric bubble features, we further support the analysis as well as the creation of a physical model. We demonstrate that our approach was successfully used to answer several research questions in the domain, and discuss its high practical relevance to identify critical seismic characteristics of fluid-saturated rock that govern its capability to store CO2.

We present a technique that conveys the uncertainty in the secondary structure of proteins-an abstraction model based on atomic coordinates. While protein data inherently contains uncertainty due to the acquisition method or the simulation algorithm, we argue that it is also worth investigating uncertainty induced by analysis algorithms that precede visualization. Our technique helps researchers investigate differences between multiple secondary structure assignment methods. We modify established algorithms for fuzzy classification and introduce a discrepancy-based approach to project an ensemble of sequences to a single importance-weighted sequence. In 2D, we depict the aggregated secondary structure assignments based on the per-residue deviation in a collapsible sequence diagram. In 3D, we extend the ribbon diagram using visual variables such as transparency, wave form, frequency, or amplitude to facilitate qualitative analysis of uncertainty. We evaluated the effectiveness and acceptance of our technique through expert reviews using two example applications: the combined assignment against established algorithms and time-dependent structural changes originating from simulated protein dynamics.

We present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.

The temporal analysis of events in a production line helps manufacturing experts get a better understanding of the line's performance and provides ideas for improvement. Especially the identification of recurring error patterns is important, because these patterns can be an indicator of systematic production issues. We present a visual analytics approach to analyze event reports of a production line. Reported events are shown as a time series plot that can be decomposed into a trend, seasonal, and remainder component by applying Seasonal Trend decomposition using Loess (STL). To find specific event patterns, the data is filtered based on aspects such as the event description or the processed product. Identified temporal patterns can be extracted from the original event series and compared visually with each other. In addition to predefined settings, experts can define a subseries of the event series and the period length of STL's seasonal component through an automatically optimized brushing of the undecomposed plot. We developed the approach together with an industry partner. To evaluate our approach, we conducted two pair analytics sessions with our industry partner's experts. We demonstrate use cases from these sessions that showcase our approach's analytical potential. Moreover, we present general expert feedback that we collected through semi-structured interviews after the pair analytics sessions.

In single-phase flow visualization, research focuses on the analysis of vector field properties. In two-phase flow, in contrast, analysis of the phase components is typically of major interest. So far, visualization research of two-phase flow concentrated on proper interface reconstruction and the analysis thereof. In this paper, we present a novel visualization technique that enables the investigation of complex two-phase flow phenomena with respect to the physics of breakup and coalescence of inclusions. On the one hand, we adapt dimensionless quantities for a localized analysis of phase instability and breakup, and provide detailed inspection of breakup dynamics with emphasis on oscillation and its interplay with rotational motion. On the other hand, we present a parametric tightly linked space-time visualization approach for an effective interactive representation of the overall dynamics. We demonstrate the utility of our approach using several two-phase CFD datasets.

Map matching is the process of assigning observed geographic positions of vehicles and their trajectories to the actual road links in a road network. In this paper, we present Visual Interactive Map Matching, a visual analytics approach to fine-tune the data preprocessing and matching process. It is based on ST-matching, a state-of-the-art and easy-to-understand map matching algorithm. Parameters of the preprocessing step and algorithm can be optimized with immediate visual feedback. Visualizations show current matching issues and performance metrics on a map and in diagrams. Manual and computer-supported editing of the road network model leads to a refined alignment of trajectories and roads. We demonstrate our approach with large-scale taxi trajectory data. We show that optimizing the matching on a subsample results in considerably improved matching quality, also when later scaled to the full dataset. An optimized matching ensures data faithfulness and prevents misinterpretation when the matched data might be investigated in follow-up analysis.

Particle data are commonly visualized by rendering a sphere for each particle. Since interactive rendering usually relies on fast local lighting, the spatial arrangement of the spheres is often very hard to perceive. That is, larger functional structures formed by the particles are not easily recognizable. Using global effects such as ambient occlusion or shadows adds important depth cues. In this work, we present Implicit Sphere Shadow Maps (ISSM), an application-tailored approach for large, dynamic particle data sets. This approach can be combined with state-of-the-art object-space ambient occlusion to further emphasize the spatial structure of molecules. We compare our technique against state-of-the-art methods for interactive rendering with respect to image quality and performance.

Two-dimensional heightfields are the most common data structure used for storing and rendering of terrain in offline rendering and especially real-time computer graphics. By its very nature, a heightfield cannot store terrain structures with multiple vertical layers such as overhanging cliffs, caves, or arches. This restriction does not apply to volumetric data structures. However, the workflow of manual modelling and editing of volumetric terrain usually is tedious and very time-consuming. Therefore, we propose to use three-dimensional curve-based primitives to efficiently model prominent, large-scale terrain features. We present a technique for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while retaining the full artistic freedom of volumetric terrains. All stages of our method are GPU-accelerated using compute shaders to ensure interactive editing of terrain.

Analysis of software performance typically takes into account clock cycles and memory consumption at each sampling point in time. Although this is a valid strategy, we argue that it is also worth investigating data and control flow structures, as observed using memory traces and call stacks, because of their importance for performance engineering. In this work, we present a visual approach to memory profiling that supports analysis of memory layout, access patterns, and aliasing in correlation to program execution. Our method leverages language-agnostic dynamic code instrumentation to minimize the impact of tracing on performance, i.e., the application remains usable on commodity hardware. The profiled data is then clustered and visualized using a density-based scatter plot. If debug symbols are available, the scatter plot is augmented by a flame graph to ease linking to the high-level source code. Our visualization helps software engineers to identify runtime behavior by relating memory addresses to instruction execution. We demonstrate our approach using a set of examples revealing different memory access patterns and discuss their influence on software performance.

Abstract This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.

Abstract In eye tracking research, finding eye movement patterns and similar strategies between participants’ eye movements is important to understand task solving strategies and obstacles. In this application paper, we present a graph comparison method using radial graphs that show Areas of Interest (AOIs) and their transitions. An analyst investigates a single graph based on dwell times, directed transitions, and temporal AOI sequences. Two graphs can be compared directly and temporal changes may be analyzed. A list and matrix approach facilitate the analyst to contrast more than two graphs guided by visually encoded graph similarities. We evaluated our approach in case studies with three eye tracking and visualization experts. They identified temporal transition patterns of eye movements across participants, groups of participants, and outliers.

Abstract We present an approach to adaptively select time steps from time‐dependent volume data sets for an integrated and comprehensive visualization. This reduced set of time steps not only saves cost, but also allows to show both the spatial structure and temporal development in one combined rendering. Our selection optimizes the coverage of the complete data on the basis of a minimum‐cost flow‐based technique to determine meaningful distances between time steps. As both optimal solutions of the involved transport and selection problem are prohibitively expensive, we present new approaches that are significantly faster with only minor deviations. We further propose an adaptive scheme for the progressive incorporation of new time steps. An interactive volume raycaster produces an integrated rendering of the selected time steps, and their computed differences are visualized in a dedicated chart to provide additional temporal similarity information. We illustrate and discuss the utility of our approach by means of different data sets from measurements and simulation.

We present a novel technique to generate transformations between arbitrary volumes, providing both expressive distances and smooth interpolates. In contrast to conventional morphing or warping approaches, our technique requires no user guidance, intermediate representations (like extracted features), or blending, and imposes no restrictions regarding shape or structure. Our technique operates directly on the volumetric data representation, and while linear programming approaches could solve the underlying problem optimally, their polynomial complexity makes them infeasible for high-resolution volumes. We therefore propose a progressive refinement approach designed for parallel execution that is able to quickly deliver approximate results that are iteratively improved toward the optimum. On this basis, we further present a new approach for the streaming selection of time steps in temporal data that allows for the reconstruction of the full sequence with a user-specified error bound. We finally demonstrate the utility of our technique for different applications, compare our approach against alternatives, and evaluate its characteristics with a variety of different data sets.

We present Molecular Surface Maps, a novel, view-independent, and concise representation for molecular surfaces. It transfers the well-known world map metaphor to molecular visualization. Our application maps the complex molecular surface to a simple 2D representation through a spherical intermediate, the Molecular Surface Globe. The Molecular Surface Map concisely shows arbitrary attributes of the original molecular surface, such as biochemical properties or geometrical features. This results in an intuitive overview, which allows researchers to assess all molecular surface attributes at a glance. Our representation can be used as a visual summarization of a molecule's interface with its environment. In particular, Molecular Surface Maps simplify the analysis and comparison of different data sets or points in time. Furthermore, the map representation can be used in a Space-time Cube to analyze time-dependent data from molecular simulations without the need for animation. We show the feasibility of Molecular Surface Maps for different typical analysis tasks of biomolecular data.

We present an integrated approach for real-time performance prediction of volume raycasting that we employ for load balancing and sampling resolution tuning. In volume rendering, the usage of acceleration techniques such as empty space skipping and early ray termination, among others, can cause significant variations in rendering performance when users adjust the camera configuration or transfer function. These variations in rendering times may result in unpleasant effects such as jerky motions or abruptly reduced responsiveness during interactive exploration. To avoid those effects, we propose an integrated approach to adapt rendering parameters according to performance needs. We assess performance-relevant data on-the-fly, for which we propose a novel technique to estimate the impact of early ray termination. On the basis of this data, we introduce a hybrid model, to achieve accurate predictions with minimal computational footprint. Our hybrid model incorporates aspects from analytical performance modeling and machine learning, with the goal to combine their respective strengths. We show the applicability of our prediction model for two different use cases: (1) to dynamically steer the sampling density in object and/or image space and (2) to dynamically distribute the workload among several different parallel computing devices. Our approach allows to reliably meet performance requirements such as a user-defined frame rate, even in the case of sudden large changes to the transfer function or the camera orientation.

Geo-tagged microblog data covers billions of movement patterns on a global and local scale. Understanding these patterns could guide urban and traffic planning or help coping with disaster situations. We present a visual analytics system to investigate travel trajectories of people reconstructed from microblog messages. To analyze seasonal changes and events and to validate movement patterns against other data sources, we contribute highly interactive visual comparison methods that normalize and contrast trajectories as well as density maps within a single view. We also compute an adaptive hierarchical graph from the trajectories to abstract individual movements into higher-level structures. Specific challenges that we tackle are, among others, the spatio-temporal sparsity of the data, the volume of data varying by region, and a diverse mix of means of transportation. The applicability of our approach is presented in three case studies.

In information visualization, evaluation plays a crucial role during the development of a new visualization technique. In recent years, eye tracking has become one means to analyze how users perceive and understand a new visualization system. Since most visualizations are highly interactive nowadays, a study should take interaction, in terms of user-input, into account as well. In addition, think aloud data gives insights into cognitive processes of participants using a visualization system. Typically, researchers evaluate these data sources separately. However, we think it is beneficial to correlate eye tracking, interaction, and think aloud data for deeper analyses. In this paper, we present challenges and possible solutions in triangulating user behavior using multiple evaluation data sources. We describe how the data is collected, synchronized, and analyzed using a string-based and a visualization-based approach founded on experiences from our current research. We suggest methods how to tackle these issues and discuss benefits and disadvantages. Thus, the contribution of our work is twofold. On the one hand, we present our approach and the experiences we gained during our research. On the other hand, we investigate additional methods that can be used to analyze this multi-source data.

In eye tracking studies a complex visual stimulus requires the definition of many areas of interest (AOIs). Often these AOIs have an inherent, nested hierarchical structure that can be utilized to facilitate analysis tasks. We discuss how this hierarchical AOI structure in combination with appropriate visualization techniques can be applied to analyze fixation sequences on differently aggregated levels. An AOI View, AOI Tree, AOI Matrix, and AOI Graph enable a bottom-up and top-down evaluation of fixation sequences. We conducted an expert review and compared our techniques to current state-of-the-art visualization techniques in eye movement research to further improve and extend our approach. To show how our approach is used in practice, we evaluate fixation sequences collected during a study where 101 AOIs are organized hierarchically.

With this demo we show a new application design for analyzing eye tracking experiments following the visual analytics approach. This application design allows users to analyze large eye tracking data sets efficiently and to find interesting patterns in eye movement data as well as correlations between eye movements and other data streams. We describe the main characteristics of the implemented visualizations and pattern recognition algorithms, present the interaction concept and demonstrate the main analysis features in a use case concerning the development of a driver assistance system.

Abstract We present a novel and efficient technique to extract Lagrangian coherent structures in two‐dimensional time‐dependent vector fields. We show that this can be achieved by employing bifurcation line extraction in the space‐time representation of the vector field, and generating space‐time bifurcation manifolds therefrom. To show the utility and applicability of our approach, we provide an evaluation of existing extraction techniques for Lagrangian coherent structures, and compare them to our approach.

Abstract In this report, we review the current state of the art of web‐based visualization applications. Recently, an increasing number of web‐based visualization applications have emerged. This is due to the fact that new technologies offered by modern browsers greatly increased the capabilities for visualizations on the web. We first review these technical aspects that are enabling this development. This includes not only improvements for local rendering like WebGL and HTML5, but also infrastructures like grid or cloud computing platforms. Another important factor is the transfer of data between the server and the client. Therefore, we also discuss advances in this field, for example methods to reduce bandwidth requirements like compression and other optimizations such as progressive rendering and streaming. After establishing these technical foundations, we review existing web‐based visualization applications and prototypes from various application domains. Furthermore, we propose a classification of these web‐based applications based on the technologies and algorithms they employ. Finally, we also discuss promising application areas that would benefit from web‐based visualization and assess their feasibility based on the existing approaches.

Processing and visualizing large scale volumetric and geometric datasets is mission critical in an increasing number of applications in academic research as well as in commercial enterprise. Often the datasets are, or can be processed to become, sparse. In this paper, we present VoxLink, a novel approach to render sparse volume data in a memory-efficient manner enabling interactive rendering on common, offthe- shelf graphics hardware. Our approach utilizes current GPU architectures for voxelizing, storing, and visualizing such datasets. It is based on the idea of perpixel linked lists (ppLL), an A-buffer implementation for order-independent transparency rendering. The method supports voxelization and rendering of dense semi-transparent geometry, sparse volume data, and implicit surface representations with a unified data structure. The proposed data structure also enables efficient simulation of global lighting effects such as reflection, refraction, and shadow ray evaluation.

Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications.

The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback.

We present a new visualization approach for displaying eye tracking data from multiple participants. We aim to show the spatio-temporal data of the gaze points in the context of the underlying image or video stimulus without occlusion. Our technique, denoted as gaze stripes, does not require the explicit definition of areas of interest but directly uses the image data around the gaze points, similar to thumbnails for images. A gaze stripe consists of a sequence of such gaze point images, oriented along a horizontal timeline. By displaying multiple aligned gaze stripes, it is possible to analyze and compare the viewing behavior of the participants over time. Since the analysis is carried out directly on the image data, expensive post-processing or manual annotation are not required. Therefore, not only patterns and outliers in the participants' scanpaths can be detected, but the context of the stimulus is available as well. Furthermore, our approach is especially well suited for dynamic stimuli due to the non-aggregated temporal mapping. Complementary views, i.e., markers, notes, screenshots, histograms, and results from automatic clustering, can be added to the visualization to display analysis results. We illustrate the usefulness of our technique on static and dynamic stimuli. Furthermore, we discuss the limitations and scalability of our approach in comparison to established visualization techniques.

The use of social media monitoring for public safety is on the brink of commercialization and practical adoption. To close the gap between research and application, this paper presents results of a two-phase study on visual analytics of social media for public safety. For the first phase, we conducted a large field study, in which 29 practitioners from disaster response and critical infrastructure management were asked to investigate crisis intelligence tasks based on Twitter data recorded during the 2013 German Flood. To this end, the ScatterBlogs visual analytics system, a platform that provides reference implementations of tools and techniques popular in research, was given to them as an integrated toolbox. We reviewed the domain experts' individual performances with the system as well as their comments about the usefulness of techniques. In the second phase, we built on this feedback about ScatterBlogs in order to sketch out a system and create additional tools specifically adapted to the collected requirements. The performance of the old lab prototype is finally compared against the re-design in a controlled user study.

Social media monitoring for crisis intelligence is on the brink of commercialization and widespread adoption. To close the gap between research and application, this paper presents results of a broad-scale case study on visual analytics of social media for situation awareness. We asked twenty-nine domain experts from disaster response and critical infrastructure management to investigate various crisis intelligence tasks based on actual Twitter data, which was recorded during the 2013 German Flood. To this end, the ScatterBlogs visual analytics framework, a platform that provides reference implementations of tools and techniques from ongoing research, was given to them as an integrated toolbox. This paper presents and reviews the domain experts' individual performances with the system, their comments about the usefulness and applicability of its capabilities, and the results of a questionnaire each participant had to complete. Based on this evaluation, we will answer the question if and how visual analytics of social media can shape tomorrow's crisis intelligence.

Word clouds provide a simple and effective means to visually communicate the most frequent words of text documents. However, only few word cloud visualizations support the contrastive analysis of multiple texts. This paper introduces Concentri Cloud, a layered word cloud layout that merges the words from several text documents into a single visualization. The weighted words are arranged in a concentric layout, with those representing the individual documents on the outer circle and the merged ones on inner circles. Interaction techniques allow to analyze the word cloud composition and to provide details on demand. The approach has been implemented and tested on several examples. A qualitative evaluation indicates the general value of Concentri Cloud and reveals benefits and limitations.

In many disciplines, such as computer aided drug design, multiple simulation runs are performed with varying parameters, yielding ensembles of data sets. Comparative visualisation of these simulation results can help understanding the influence of different parameters. However, researchers might need to compare large numbers of variants. Single desktop monitors often do not have the resolution and screen size required for showing a whole ensemble at once with sufficient detail. Wall-sized high-resolution displays can be a solution for this problem. Although a number of studies has been conducted on how large high-resolution displays affect the speed and accuracy of certain tasks, only few of them are related to actual scientific visualisation tasks. We built a system for comparative visualisation of simulation results that can be used with conventional desktop monitors and with large high-resolution displays. We conducted a study using biochemical simulation data to evaluate the impact of screen size and 3D stereo display on a comparison task.

The term code ownership is used in software engineering to describe who authored a certain piece of software. Code ownership is commonly determined by investigating the data from version control systems, defining the owner as the person who contributed the most lines to a file, module, etc. Existing visualisation for ownership usually relies on per-line annotations from the version control system, thus only conveying the information who changed a line the last time, potentially adding some visual cue about how old the respective change is. This, however, can be misleading, because any change of even a single character changes ownership. In this paper, we propose a visualisation that accounts for fractional ownership changes over time on a per-character basis. Our technique incorporates visual cues to convey that typical definitions of ownership have an inherent uncertainty and provides details on the cause of this uncertainty on demand. For our definition of code ownership being a low-level one, we propose implementing the visualisation as an in-situ visualisation in the code editor of modern development environments. We will show examples of the efficacy of our approach and discuss its advantages and disadvantages compared to conventional line-based ownership.

Scientific data is typically analyzed using visualizations. Large high-resolution displays can facilitate the analysis process since they offer more screen real estate than desktop monitors, which allows visualizing more data in greater detail and working collaboratively. On the other hand, mobile devices like smartphones and tablets are getting more and more popular but do not have sufficient graphics capabilities for advanced scientific visualizations. In this paper, we discuss a client application for Android devices that allows to remote control a scientific visualization framework. Combining the power of remote rendering with the availability, flexibility, and mobility of mobile devices results in a ubiquitous computing solution for scientific visualization. The user interaction with a large high-resolution display is only one possible application scenario. Other scenarios include presentation of results and remote visualization. The design of our Android application fulfills the requirement of all these application scenarios.

Graph visualization is essential for the analysis of networks and relational data sets. Often, most of the effort is expended on computing sophisticated layouts of the visual representation of the graph. Even though this is increasingly accelerated by use of graphics processing units (GPUs), the rendering is often considered as circumstantial. In this paper, we present a coherent approach to graph visualization that utilizes all features of modern GPUs. We describe specialized data structures and our GPU-centric pipeline for computing and rendering a layout, while enabling steering and interaction. We evaluate technical aspects of our approach as well as its applicability to huge real-world graphs.

Abstract We present a novel physically‐based method to visualize stress tensor fields. By incorporating photoelasticity into traditional raycasting and extending it with reflection and refraction, taking into account polarization, we obtain the virtual counterpart to traditional experimental polariscopes. This allows us to provide photoelastic analysis of stress tensor fields in arbitrary domains. In our model, the optical material properties, such as stress‐optic coefficient and refractive index, can either be chosen in compliance with the subject under investigation, or, in case of stress problems that do not model optical properties or that are not transparent, be chosen according to known or even new transparent materials. This enables direct application of established polariscope methodology together with respective interpretation. Using a GPU‐based implementation, we compare our technique to experimental data, and demonstrate its utility with several simulated datasets.

Abstract Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information platform that supports submission of user queries related to environmental conditions and orchestrates results from complementary services to generate personalized suggestions. The system discovers and processes reliable data in the Web in order to convert them into knowledge. At runtime, this information is transferred into an ontology‐structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. The platform is demonstrated with real world use cases in the south area of Finland, showing the impact it can have on the quality of everyday life.

When people look at pictures, they fixate on specific areas. The sequences of such fixations are so characteristic for certain pictures that metrics can be derived that allow successful grouping of similar pieces of visual art. However, determining enough fixation sequences by eye tracking is not practically feasible for large groups of people and pictures. In order to get around this limitation, we present a novel algorithm that simulates eye movements by calculating scan paths for images and time frames in real time. The basis of our algorithm is an attention model that combines and optimizes rectangle features with Adaboost. The model is adapted to the characteristics of the retina, and its input is dependent on a few earlier fixations. This method results in significant improvements compared to previous approaches. Our simulation process delivers the same data structures as an eye tracker, thus can be analyzed by standard eye-tracking software. A comparison with recorded data from eye tracking experiments shows that our algorithm for simulating fixations has a very good prediction quality for the stimulus areas on which many subjects focus. We also compare the results with those from earlier works. Finally, we demonstrate how the presented algorithm can be used to calculate the similarity of pictures in terms of human perception.

Visualization applications nowadays not only face increasingly larger datasets, but have to solve increasingly complex research questions. They often require more than a single algorithm and consequently a software solution will exceed the possibilities of simple research prototypes. Well-established systems intended for such complex visual analysis purposes have usually been designed for classical, mesh-based graphics approaches. For particle-based data, however, existing visualization frameworks are too generic - e.g. lacking possibilities for consistent low-level GPU optimization for high-performance graphics - and at the same time are too limited - e.g. by enforcing the use of structures suboptimal for some computations. Thus, we developed the system softwareMegaMol for visualization research on particle-based data. On the one hand, flexible data structures and functional module design allow for easy adaption to changing research questions, e.g. studying vapors in thermodynamics, solid material in physics, or complex functional macromolecules like proteins in biochemistry. Therefore, MegaMol is designed as a development framework. On the other hand, common functionality for data handling and advanced rendering implementations are available and beneficial for all applications. We present several case studies of work implemented using our system as well as a comparison to other freely available or open source systems.

In recent years, many approaches have been developed that efficiently and effectively visualize movement data, e.g., by providing suitable aggregation strategies to reduce visual clutter. Analysts can use them to identify distinct movement patterns, such as trajectories with similar direction, form, length, and speed. However, less effort has been spent on finding the semantics behind movements, i.e. why somebody or something is moving. This can be of great value for different applications, such as product usage and consumer analysis, to better understand urban dynamics, and to improve situational awareness. Unfortunately, semantic information often gets lost when data is recorded. Thus, we suggest to enrich trajectory data with POI information using social media services and show how semantic insights can be gained. Furthermore, we show how to handle semantic uncertainties in time and space, which result from noisy, unprecise, and missing data, by introducing a POI decision model in combination with highly interactive visualizations. Finally, we evaluate our approach with two case studies on a large electric scooter data set and test our model on data with known ground truth.

We present a visual analysis application for protein simulations that allows researchers to investigate dynamic data interactively. Special focus lies on the analysis of cavities and binding sites, which play a critical role in protein function. Cavities are extracted and classified. The surface area of all cavities and the diameter of pockets and channels is computed to provide evidence for their accessibility. These values are also plotted in 2D graphs for a quantitative time-dependent analysis. For dynamic simulation data sets, the cavities are tracked to show their stability over time. The user is provided with a range of application-related parameters to interactively adjust the analysis algorithms. A sequence diagram shows the structure of the protein and additional annotations like binding sites. Furthermore, all views support brushing and linking for consistent selection and filtering. All algorithmic steps are implemented to run interactively on a commodity workstation. As a result, the user can immediately see the effect of a parameter change. This enables the real-time analysis of a running simulation for in-situ visualization.

With increasing use of GPS devices more and more location-based information is accessible. Thus not only more movements of people are tracked but also POI (point of interest) information becomes available in increasing geo-spatial density. To enable analysis of movement behavior, we present an approach to enrich trajectory data with semantic POI information and show how additional insights can be gained. Using a density-based clustering technique we extract 1.215 frequent destinations of ~150.000 user movements from a large e-mobility database. We query available context information from Foursquare, a popular location-based social network, to enrich the destinations with semantic background. As GPS measurements can be noisy, often more then one possible destination is found and movement patterns vary over time. Therefore we present highly interactive visualizations that enable an analyst to cope with the inherent geospatial and behavioral uncertainties.

As systems grow at an increasing pace to keep up with the demand for larger data and ever more detailed simulations, failures also occur more frequently on various levels. Traditionally, fault tolerance for visualization is either disregarded due to performance reasons or handled analogously to the state of the art for high performance computing (HPC), for example rollback-recovery. In this paper we summarize the inherent challenges of distributed visualization as well as the applicability and differences of fault tolerance methods used in HPC and distributed visualization. We derive complementary methods to increase the resilience of such systems and present visualization specific methods for failure semantics and masking.

Increasing amounts of information are being made available as RDF datasets. While a variety of techniques for finding and browsing single resources from such datasets exists, getting an idea of what data is generally available in a given RDF graph is often problematic. With filter dials, we propose a novel visualization that shows the sizes of various result sets based on different combinations of filter criteria in a compact way. Result sets of one filter dial can be used as input for another filter dial, thereby forming complex chained filter expressions. After the description of the concept, a brief example based on bibliographical data is presented.

SPARQL is currently the major query language for the Semantic Web. However, writing SPARQL queries is not an easy task and requires some understanding of technologies like RDF. In order to enable users without this knowledge to query linked data, visual interfaces are required that hide the SPARQL syntax and provide graphical support for query building. Based on the concept of extended filter/flow graphs, we present a novel approach for visual querying that addresses the unique specifics of SPARQL and RDF. In particular, it enables the creation of SELECT and ASK queries, though it can also be used for other query forms. In contrast to related work, the users do not need to provide any structured text input but can create the queries entirely with graphical elements. Our approach supports most features of SPARQL and hence also the construction of complex query expressions. It has been implemented in a visual querying framework and tested on different RDF datasets, including DBpedia that is used as an example in this paper. Since the filter/flow concept is empirically well-founded, we expect our approach to be very usable, which is additionally supported by the results of a qualitative user study we conducted.

Recording of eye movement data can help to understand where and at what participants look. However, analyzing eye movement data is a time consuming task. Using visualization techniques in the analysis process can help to uncover concealed relationships within the data and can therefore be seen as one means in the analysis of eye movement data. The most well known visualization techniques in eye tracking are heat maps or scanpaths. In recent years more visualization techniques have been developed, as for example scaled traces [Goldberg and Helfman 2010], eyePatterns [West et al. 2006], or eSeeTrack [Tsang et al. 2010].

Several algorithms, approaches, and implementations have been developed to support comparison of scan paths and finding of interesting scan path structures. In this work we contribute a visual approach to support scan path comparison. A key feature of this approach is the combination of a clustering algorithm using Levenshtein distance with the parallel scan path visualization technique. The combination of computational methods with an interactive visualization allows us to use both the power of pattern finding algorithms and the human ability to visually recognize patterns. To use the concept in practice we implemented the approach in a prototype and show its application in two scan path analysis scenarios from automobile usability testing and visualization research.

Several algorithms, approaches, and implementations have been developed to support comparison of scan paths and finding of interesting scan path structures. In this work we contribute a visual approach to support scan path comparison. A key feature of this approach is the combination of a clustering algorithm using Levenshtein distance with the parallel scan path visualization technique. The combination of computational methods with an interactive visualization allows us to use both the power of pattern finding algorithms and the human ability to visually recognize patterns. To use the concept in practice we implemented the approach in a prototype and show its application in two scan path analysis scenarios from automobile usability testing and visualization research.

The visualization of relational data by node-link diagrams quickly leads to a degradation of performance at some exploration tasks when the diagrams show visual clutter and overdraw. To address this challenge of large-data graph visualization, we introduce Graph Metric Views, a technique that enriches the visualization of traditional layout strategies for node-link diagrams by additionally allowing an analyst to interactively explore graph-specific metrics such as number of nodes, number of link crossings, link coverage, or degree of orthogonality. To this end, we support an analyst with additional histogram-like representations at the axes of the display space for graph-specific metrics. In this way, a cluttered and densely packed node-link diagram becomes more explorable even for dense graph regions: The user can use the distribution of metric values as an overview and then select regions of interest for further investigation and filtering.

Visualization and interactive analysis can help network administrators and security analysts analyze the network flow and log data. The complexity of such an analysis requires a combination of knowledge and experience from more domain experts to solve difficult problems faster and with higher reliability. We developed an online visual analysis system called OCEANS to address this topic by allowing close collaboration among security analysts to create deeper insights in detecting network events. Loading the heterogeneous data source (netflow, IPS log and host status log), OCEANS provides a multi-level visualization showing temporal overview, IP connections and detailed connections. Participants can submit their findings through the visual interface and refer to others' existing findings. Users can gain inspiration from each other and collaborate on finding subtle events and targeting multi-phase attacks. Our case study confirms that OCEANS is intuitive to use and can improve efficiency. The crowd collaboration helps the users comprehend the situation and reduce false alarms.

Abstract For the rendering of multiple scattering effects in participating media, methods based on the diffusion approximation are an extremely efficient alternative to Monte Carlo path tracing. However, in sufficiently transparent regions, classical diffusion approximation suffers from non‐physical radiative fluxes which leads to a poor match to correct light transport. In particular, this prevents the application of classical diffusion approximation to heterogeneous media, where opaque material is embedded within transparent regions. To address this limitation, we introduce flux‐limited diffusion, a technique from the astrophysics domain. This method provides a better approximation to light transport than classical diffusion approximation, particularly when applied to heterogeneous media, and hence broadens the applicability of diffusion‐based techniques. We provide an algorithm for flux‐limited diffusion, which is validated using the transport theory for a point light source in an infinite homogeneous medium. We further demonstrate that our implementation of flux‐limited diffusion produces more accurate renderings of multiple scattering in various heterogeneous datasets than classical diffusion approximation, by comparing both methods to ground truth renderings obtained via volumetric path tracing.

Abstract We present a novel line drawing algorithm that illustrates surfaces in real‐time to convey their shape. We use line integral convolution (LIC) and employ ambient occlusion for illustrative surface rendering. Furthermore, our method depicts salient regions based on the illumination gradient. Our method works on animated surfaces in a frame‐coherent manner. Therefore, it yields an illustrative representation of time‐dependent surfaces as no preprocessing step is needed. In this paper, the method is used to highlight the structure of molecular surfaces and to illustrate important surface features like cavities, channels, and pockets. The benefit of our method was evaluated with domain experts. We also demonstrate the applicability of our method to medical visualization.

Abstract The comparison of molecular surface attributes is of interest for computer aided drug design and the analysis of biochemical simulations. Due to the non‐rigid nature of molecular surfaces, partial shape matching is feasible for mapping two surfaces onto each other. We present a novel technique to obtain a mapping relation between two surfaces using a deformable model approach. This relation is used for pair‐wise comparison of local surface attributes (e.g. electrostatic potential). We combine the difference value as well as the comparability as derived from the local matching quality in a 3D molecular visualization by mapping them to color. A 2D matrix shows the global dissimilarity in an overview of different data sets in an ensemble. We apply our visualizations to simulation results provided by collaborators from the field of biochemistry to evaluate the effectiveness of our results.

We present a novel scheme for progressive rendering in interactive visualization. Static settings with respect to a certain image quality or frame rate are inherently incapable of delivering both high frame rates for rapid changes and high image quality for detailed investigation. Our novel technique flexibly adapts by steering the visualization process in three major degrees of freedom: when to terminate the refinement of a frame in the background and start a new one, when to display a frame currently computed, and how much resources to consume. We base these decisions on the correlation of the errors due to insufficient sampling and response delay, which we estimate separately using fast yet expressive heuristics. To automate the configuration of the steering behavior, we employ offline video quality analysis. We provide an efficient implementation of our scheme for the application of volume raycasting, featuring integrated GPU-accelerated image reconstruction and error estimation. Our implementation performs an integral handling of the changes due to camera transforms, transfer function adaptations, as well as the progression of the data to in time. Finally, the overall technique is evaluated with an expert study.

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

We present a technique to visualize the streamline-based mapping between the boundary of a simply-connected subregion of arbitrary 3D vector fields. While the streamlines are seeded on one part of the boundary, the remaining part serves as escape border. Hence, the seeding part of the boundary represents a map of streamline behavior, indicating if streamlines reach the escape border or not. Since the resulting maps typically exhibit a very fine and complex structure and are thus not amenable to direct sampling, our approach instead aims at topologically consistent extraction of their boundary. We show that isocline surfaces of the projected vector field provide a robust basis for stream-surface-based extraction of these boundaries. The utility of our technique is demonstrated in the context of transport processes using vector field data from different domains.

Piecewise linear interface calculation (PLIC) is one of the most widely employed reconstruction schemes for the simulation of multiphase flow. In this visualization paper we focus on the reconstruction from the simulation point of view, i.e., we present a framework for the analysis of this reconstruction scheme together with its implications on the overall simulation. By interpreting PLIC reconstruction as an isosurface extraction problem from the first-order Taylor approximation of the underlying volume of fluid field, we obtain a framework for error analysis and geometric representation of the reconstruction including the fluxes involved in the simulation. At the same time this generalizes PLIC to higher-order approximation. We exemplify the utility and versatility of our visualization approach on several multiphase CFD examples.

Abstract Molecular visualization is an important tool for analysing the results of biochemical simulations. With modern GPU ray casting approaches, it is only possible to render several million of atoms interactively unless advanced acceleration methods are employed. Whole‐cell simulations consist of at least several billion atoms even for simplified cell models. However, many instances of only a few different proteins occur in the intracellular environment, which can be exploited to fit the data into the graphics memory. For each protein species, one model is stored and rendered once per instance. The proposed method exploits recent algorithmic advances for particle rendering and the repetitive nature of intracellular proteins to visualize dynamic results from mesoscopic simulations of cellular transport processes. We present two out‐of‐core optimizations for the interactive visualization of data sets composed of billions of atoms as well as details on the data preparation and the employed rendering techniques. Furthermore, we apply advanced shading methods to improve the image quality including methods to enhance depth and shape perception besides non‐photorealistic rendering methods. We also show that the method can be used to render scenes that are composed of triangulated instances, not only implicit surfaces.

Abstract We present a coordinated‐view application for the analysis of molecular surface features like cavities, channels and pockets. Our tool employs object‐space ambient occlusion for the detection of such features and tracks them over time. It offers time‐dependent graphs of metrics concerning those features and allows analyzing the temporal relationship of the features, i.e. when they (dis)appear, split or merge and which features participate in each of these events. The automated analysis process is performed in real time while the user interactively explores a dynamic data set. The system supports linking and brushing to allow for a user‐guided visual analysis based on different aspects of the data. We demonstrate the effectiveness of our approach by applying it to data sets from biochemistry and report the insights that can be gained. We also evaluate the benefits of our method with respect to recent advancements in the field. The algorithmic pipeline leverages the computing power of modern GPUs, thus achieving interactive frame rates without any precomputation for fully dynamic data sets.

Abstract The visual analysis of spatiotemporal movement is a challenging task. There may be millions of routes of different length and shape with different origin and destination, extending over a long time span. Furthermore there can be various correlated attributes depending on the data domain, e.g. engine measurements for mobility data or sensor data for animal tracking. Visualizing such data tends to produce cluttered and incomprehensible images that need to be accompanied by sophisticated filtering methods. We present TrajectoryLenses, an interaction technique that extends the exploration lens metaphor to support complex filter expressions and the analysis of long time periods. Analysts might be interested only in movements that occur in a given time range, traverse a certain region, or end at a given area of interest (AOI). Our lenses can be placed on an interactive map to identify such geospatial AOIs. They can be grouped with set operations to create powerful geospatial queries. For each group of lenses, users can access aggregated data for different attributes like the number of matching movements, covered time, or vehicle performance. We demonstrate the applicability of our technique on a large, real‐world dataset of electric scooter tracks spanning a 2‐year period.

The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios.

It was shown recently how the 2D vector field topology concept, directly applicable to stationary vector fields only, can be generalized to time-dependent vector fields by replacing the role of stream lines by streak lines. The present paper extends this concept to 3D vector fields. In traditional 3D vector field topology separatrices can be obtained by integrating stream lines from 0D seeds corresponding to critical points. We show that in our new concept, in contrast, 1D seeding constructs are required for computing streak-based separatrices. In analogy to the 2D generalization we show that invariant manifolds can be obtained by seeding streak surfaces along distinguished path surfaces emanating from intersection curves between codimension-1 ridges in the forward and reverse finite-time Lyapunov exponent (FTLE) fields. These path surfaces represent a time-dependent generalization of critical points and convey further structure in time-dependent topology of vector fields. Compared to the traditional approach based on FTLE ridges, the resulting streak manifolds ease the analysis of Lagrangian coherent structures (LCS) with respect to visual quality and computational cost, especially when time series of LCS are computed. We exemplify validity and utility of the new approach using both synthetic examples and computational fluid dynamics results.

In many different application fields particle-based simulation, like molecular dynamics, are used to study material properties and behavior. Nowadays, simulation data sets consist of millions of particles and thousands of time steps challenging interactive visualization. Direct glyph-based representations of the particle data are important for the visual analysis process and these rendering methods can be optimized to be able to work sufficiently fast with huge data sets. However, the perception of the implicit spatial structures formed by such data is often hindered by aliasing and visual clutter. Especially the depth of these structures can be grasped better if visual cues are applied, even in interactive representations. We hence present a method to apply object-space ambient occlusion, based on local neighborhood information, to large timedependent particle-based data sets without the need for any precomputations. Based on density information collected in real-time, glyph-based representations of the data sets can be visually enhanced without significant impact on the rendering performance allowing to visualize multi-million particle data sets interactively on commodity workstations.

Analyzing message streams from social blogging services such as Twitter is a challenging task because of the vast number of documents that are produced daily. At the same time, the availability of geolocated, realtime, and manually created status updates are an invaluable data source for situational awareness scenarios. In this work we present an approach that allows for an interactive analysis of location-based microblog messages in realtime by means of scalable aggregation and geolocated text visualization. For this purpose, we use a novel cluster analysis approach and distinguish between local event reports and global media reaction to detect spatiotemporal anomalies automatically. A workbench allows the scalable visual examination and analysis of messages featuring perspective and semantic layers on a world map representation. Our novel techniques can be used by analysts to classify the presented event candidates and examine them on a global scale.

Eye tracking analysis is the state of the art technique to study questions of usability and cognition of graphical user interfaces. This paper presents a new technique for the visualization of eye tracking data, the Parallel Scan-Path Visualization. A key feature is the visualization of eye movements of many subjects on a single screen in a parallel layout. The visualization presents various properties of scan-paths, such as fixations, gaze durations and eye shift frequencies at one glance. The paper concludes with an example of use of the Parallel Scan-Path Visualization technique.

High performance computing still typically disregards heterogeneous environments and focuses on homogeneous clusters. While providing advantages and easing development, this fails to address the recent change toward heterogeneous computing infrastructure. With DIANA we presented an abstraction layer for unified access to local compute hardware including different hardware APIs. In this paper we extend DIANA, enabling transparent access to remote hardware. In this context, we demonstrate how to use DIANA for distributed computation and large-scale visualization of the finite-time Lyapunov exponent field in heterogeneous environments.

The global scheduler of a current GPU distributes thread blocks to symmetric multiprocessors (SM), which schedule threads for execution with the granularity of a warp. Threads in a warp execute the same code path in lockstep, which potentially leads to a large amount of wasted cycles for divergent control flow. In order to overcome this general issue of SIMT architectures, we propose techniques to relax divergence on the fly within a computation kernel in order to achieve a much higher total utilization of processing cores. We propose techniques for branch and loop divergence (which may also be combined) switching to suitable tasks during a GPU kernel run every time divergence occurs. Our newly introduced techniques can easily be applied to arbitrary iterative algorithms and we evaluate the performance and effectiveness of our approach exemplarily via synthetic and real world applications.

Although the filter/flow model is a useful concept for query visualization, the resulting graphs can be quite complex. We aim to reduce this complexity by substituting recurring subgraphs with more compact structures. Based on related work on extensions to the filter/flow concept, we have identified four recurring subgraphs that can be significantly simplified by substitutions. We describe these substitutions and discuss the preconditions that must be satisfied for their application. An example of news filtering illustrates how the substitutions can be used to simplify the overall structure of filter/flow graphs and increase their readability.

Abstract Advection has been the standard transport mechanism in flow visualization. Diffusion, in contrast, has not been considered important in visual flow field analysis so far, although it is inherent to many physical processes. We present a novel technique that allows for interactive 3D visualization of both advection and diffusion in unsteady fluid flow. We extend texture‐based flow visualization, which is advection‐oriented, by diffusion. Our finite volume approach based on WENO (weighted essentially non‐oscillatory) reconstruction is well parallelizable and features low numerical diffusion at interactive rates. Our scheme contributes to three different applications: (a) high‐quality dye advection at low numerical diffusion, (b) physically‐based dye advection accounting for diffusivity of virtual media, and (c) visualization of advection‐diffusion fluxes in physical media where the velocity field is accompanied by a concentration field. Interactive rendering of the virtual dye is accomplished by ray casting. We apply our GPU implementation to CFD examples of thermal convection and evaporation phenomena.

This paper presents a visualization approach for detecting and exploring similarity in the temporal variation of field data. We provide an interactive technique for extracting correlations from similarity matrices which capture temporal similarity of univariate functions. We make use of the concept to extract periodic and quasiperiodic behavior at single (spatial) points as well as similarity between different locations within a field and also between different data sets. The obtained correlations are utilized for visual exploration of both temporal and spatial relationships in terms of temporal similarity. Our entire pipeline offers visual interaction and inspection, allowing for the flexibility that in particular time-dependent data analysis techniques require. We demonstrate the utility and versatility of our approach by applying our implementation to data from both simulation and measurement.

Metal oxides are important for many technical applications. For example alumina (aluminum oxide) is the most commonly-used ceramic in microelectronic devices thanks to its excellent properties. Experimental studies of these materials are increasingly supplemented with computer simulations. Molecular dynamics (MD) simulations can reproduce the material behavior very well and are now reaching time scales relevant for interesting processes like crack propagation. In this work we focus on the visualization of induced electric dipole moments on oxygen atoms in crack propagation simulations. The straightforward visualization using glyphs for the individual atoms, simple shapes like spheres or arrows, is insufficient for providing information about the data set as a whole. As our contribution we show for the first time that fractional anisotropy values computed from the local neighborhood of individual atoms of MD simulation data depict important information about relevant properties of the field of induced electric dipole moments. Iso surfaces in the field of fractional anisotropy as well as adjustments of the glyph representation allow the user to identify regions of correlated orientation. We present novel and relevant findings for the application domain resulting from these visualizations, like the influence of mechanical forces on the electrostatic properties.

Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analyst's information need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifier's quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora.

Molecular dynamics is a widely used simulation technique to investigate material properties and structural changes under external forces. The availability of more powerful clusters and algorithms continues to increase the spatial and temporal extents of the simulation domain. This poses a particular challenge for the visualization of the underlying processes which might consist of millions of particles and thousands of time steps. Some application domains have developed special visual metaphors to only represent the relevant information of such data sets but these approaches typically require detailed domain knowledge that might not always be available or applicable. We propose a general technique that replaces the huge amount of simulated particles by a smaller set of representatives that are used for the visualization instead. The representatives capture the characteristics of the underlying particle density and exhibit coherency over time. We introduce loose capacity-constrained Voronoi diagrams for the generation of these representatives by means of a GPU-friendly, parallel algorithm. This way we achieve visualizations that reflect the particle distribution and geometric structure of the original data very faithfully. We evaluate our approach using real-world data sets from the application domains of material science, thermodynamics and dynamical systems theory.

Recent advances in experimental structure determination provide a wealth of structural data on huge macromolecular assemblies such as the ribosome or viral capsids, available in public databases. Further structural models arise from reconstructions using symmetry orders or fitting crystal structures into low-resolution maps obtained by electron-microscopy or small angle X-ray scattering experiments. Visual inspection of these huge structures remains an important way of unravelling some of their secrets. However, such visualization cannot conveniently be carried out using conventional rendering approaches, either due to performance limitations or due to lack of realism. Recent developments, in particular drawing benefit from the capabilities of Graphics Processing Units (GPUs), herald the next generation of molecular visualization solutions addressing these issues. In this article, we present advances in computer science and visualization that help biologists visualize, understand and manipulate large and complex molecular systems, introducing concepts that remain little-known in the bioinformatics field. Furthermore, we compile currently available software and methods enhancing the shape perception of such macromolecular assemblies, for example based on surface simplification or lighting ameliorations.

Abstract We present a novel application for the interactive exploration of cavities within proteins in dynamic data sets. Inside a protein, cavities can often be found close to the active center. Therefore, when analyzing a molecular dynamics simulation trajectory it is of great interest to find these cavities and determine if such a cavity opens up to the environment, making the binding site accessible to the surrounding substrate. Our user‐driven approach enables expert users to select a certain cavity and track its evolution over time. The user is supported by different visualizations of the extracted cavity to facilitate the analysis. The boundary of the protein and its cavities is obtained by means of volume ray casting, where the volume is computed in real‐time for each frame, therefore allowing the examination of time‐dependent data sets. A fast, partial segmentation of the volume is applied to obtain the selected cavity and trace it over time. Domain experts found our method useful when they applied it exemplarily on two trajectories of lipases from Rhizomucor miehei and Candida antarctica. In both data sets cavities near the active center were easily identified and tracked over time until they reached the surface and formed an open substrate channel.

Abstract The parallel vectors (PV) operator is a feature extraction approach for defining line‐type features such as creases (ridges and valleys) in scalar fields, as well as separation, attachment, and vortex core lines in vector fields. In this work, we extend PV feature extraction to higher‐order data represented by piecewise analytical functions defined over grid cells. The extraction uses PV in two distinct stages. First, seed points on the feature lines are placed by evaluating the inclusion form of the PV criterion with reduced affine arithmetic. Second, a feature flow field is derived from the higher‐order PV expression where the features can be extracted as streamlines starting at the seeds. Our approach allows for guaranteed bounds regarding accuracy with respect to existence, position, and topology of the features obtained. The method is suitable for parallel implementation and we present results obtained with our GPU‐based prototype. We apply our method to higher‐order data obtained from discontinuous Galerkin fluid simulations.

AS my second two-year term as Editor-in-Chief (EIC) of the IEEE Transactions on Visualization and Computer Graphics (TVCG) is ending, it is my pleasure to introduce Ming C. Lin as the new EIC. TVCG is very fortunate to have such an excellent and highly visible researcher and experienced leader to guide it. Dr. Lin has been on the editorial board for the past seven years and has also served as Associate EIC of TVCG for the last four years. This past year, an international search for a new EIC was conducted. The search committee received nominations from several wellqualifi ed candidates and eventually made their recommendations to the IEEE Computer Society (CS) Publications Board, with the offi cial appointment being made by the President of the IEEE Computer Society. In refl ecting on the changes and expansion of TVCG over my tenure as EIC, I am pleased to say that we have succeeded in increasing the publication frequency (from bimonthly to monthly), signifi cantly increasing the number of published papers per year, decreasing the time from submission to fi rst review decision and publication of the preprint in the IEEE CS Digital Library. The journal has also increased the number of special sections that are based on signifi cantly extended versions of the best papers from various conferences and symposia in our fi eld. Obviously, this attracts high-quality research papers from different research areas within TVCG’s scope and contributes to the excellent reputation of TVCG among the attendees of these events. The partnership with the IEEE Visualization and Graphics Technical Committee (VGTC) on publishing the papers accepted for the IEEE Visualization and Information Visualization Conferences after a rigorous journal-style two-phase review cycle into a special issue of TVCG resulted in a very positive development for both the journal and the conferences under the auspices of VGTC. This success model is now also considered by other journals and conferences, and plans for including a few more high-quality conferences into TVCG are under way. Including more outstanding content into the journal is not only attractive to the readers, but is also essential for the clear upward trend of TVCG’s impact factor. I am grateful to have had the opportunity to be a part of this successful growth of TVCG. It has been an extremely rewarding experience. The continued growth of the journal is the culmination of work by many individuals and groups. First of all, the success of the journal relies on the authors and readers of TVCG for their high-quality submissions and their continued support as subscribers. The journal cofounders and previous EICs started TVCG on a great course from its inception and have continued to provide valuable advice and guidance. The Associate Editors and reviewers over the past 16 years have succeeded in providing effi cient, high-quality review and guidance in creating excellent archival papers. I am indebted to my extraordinary Associate EICs, Ming Lin and Baining Guo, as well as the great Associate Editors that I have had the opportunity to work with over the past four years. Their work is invaluable to the success of TVCG. The CS publications staff, especially Alicia Stickley, Erin Espriu, Hilda Carman, Joyce Arnold, and Kathy Santa Maria, and Andy Morton from Allen Press as the TVCG Administrator, have been great colleagues and I am extremely thankful for their support and hard work: TVCG would not exist without them and this will continue to be true in the OnlinePlus era of the journal. I look forward to watching TVCG continue to grow under Dr. Lin’s guidance over the next four years and I wish her the very best for this strenuous but rewarding job in these exciting times of academic publishing.

Patents are of growing importance in current economic markets. Analyzing patent information has, therefore, become a common task for many interest groups. As a prerequisite for patent analysis, extensive search for relevant patent information is essential. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. Already the amount of patent data to be analyzed poses challenges with respect to scalability. Further scalability issues arise concerning the diversity of users and the large variety of analysis tasks. With "PatViz", a system for interactive analysis of patent information has been developed addressing scalability at various levels. PatViz provides a visual environment allowing for interactive reintegration of insights into subsequent search iterations, thereby bridging the gap between search and analytic processes. Because of its extensibility, we expect that the approach we have taken can be employed in different problem domains that require high quality of search results regarding their completeness.

Abstract Molecular dynamics simulations are a principal tool for studying molecular systems. Such simulations are used to investigate molecular structure, dynamics, and thermodynamical properties, as well as a replacement for, or complement to, costly and dangerous experiments. With the increasing availability of computational power the resulting data sets are becoming increasingly larger, and benchmarks indicate that the interactive visualization on desktop computers poses a challenge when rendering substantially more than millions of glyphs. Trading visual quality for rendering performance is a common approach when interactivity has to be guaranteed. In this paper we address both problems and present a method for high‐quality visualization of massive molecular dynamics data sets. We employ several optimization strategies on different levels of granularity, such as data quantization, data caching in video memory, and a two‐level occlusion culling strategy: coarse culling via hardware occlusion queries and a vertex‐level culling using maximum depth mipmaps. To ensure optimal image quality we employ GPU raycasting and deferred shading with smooth normal vector generation. We demonstrate that our method allows us to interactively render data sets containing tens of millions of high‐quality glyphs.

Abstract Higher‐order finite element methods have emerged as an important discretization scheme for simulation. They are increasingly used in contemporary numerical solvers, generating a new class of data that must be analyzed by scientists and engineers. Currently available visualization tools for this type of data are either batch oriented or limited to certain cell types and polynomial degrees. Other approaches approximate higher‐order data by resampling resulting in trade‐offs in interactivity and quality. To overcome these limitations, we have developed a distributed visualization system which allows for interactive exploration of non‐conforming unstructured grids, resulting from space‐time discontinuous Galerkin simulations, in which each cell has its own higher‐order polynomial solution. Our system employs GPU‐based raycasting for direct volume rendering of complex grids which feature non‐convex, curvilinear cells with varying polynomial degree. Frequency‐based adaptive sampling accounts for the high variations along rays. For distribution across a GPU cluster, the initial object‐space partitioning is determined by cell characteristics like the polynomial degree and is adapted at runtime by a load balancing mechanism. The performance and utility of our system is evaluated for different aeroacoustic simulations involving the propagation of shock fronts.

The three papers in this special section were originally presented at the IEEE Symposium on Visual Analytics Science and Technology (VAST) in Columbus, Ohio, in October 2008.

In this paper, we propose a stochastic simulation to model and analyze cellular signal transduction. The high number of objects in a simulation requires advanced visualization techniques: first to handle the large data sets, second to support the human perception in the crowded environment, and third to provide an interactive exploration tool. To adjust the state of the cell to an external signal, a specific set of signaling molecules transports the information to the nucleus deep inside the cell. There, key molecules regulate gene expression. In contrast to continuous ODE models we model all signaling molecules individually in a more realistic crowded and disordered environment. Beyond spatiotemporal concentration profiles our data describes the process on a mesoscopic, molecular level, allowing a detailed view of intracellular events. In our proposed schematic visualization individual molecules, their tracks, or reactions can be selected and brought into focus to highlight the signal transduction pathway. Segmentation, depth cues and depth of field are applied to reduce the visual complexity. We also provide a virtual microscope to display images for comparison with wet lab experiments. The method is applied to distinguish different transport modes of MAPK (mitogen-activated protein kinase) signaling molecules in a cell. In addition, we simulate the diffusion of drug molecules through the extracellular space of a solid tumor and visualize the challenges in cancer related therapeutic drug delivery.

Particle-based simulations are a popular tool for researchers in various sciences. In combination with the availability of ever larger COTS clusters and the consequently increasing number of simulated particles the resulting datasets pose a challenge for real-time visualization. Additionally the semantic density of the particles exceeds the possibilities of basic glyphs, like splats or spheres and results in dataset sizes larger by at least an order of magnitude. Interactive visualization on common workstations requires a careful optimization of the data management, especially of the transfer between CPU and GPU. We propose a flexible benchmarking tool along with a series of tests to allow the evaluation of the performance of different CPU/GPU combinations in relation to a particular implementation. We evaluate different uploading strategies and rendering methods for point-based compound glyphs suitable for representing the aforementioned datasets. CPU and GPU-based approaches are compared with respect to their rendering and storage efficiency to point out the optimal solution when dealing with time-dependent datasets. The results of our research are of general interest since they can be transferred to other applications where CPU-GPU bandwidth and a high number of graphical primitives per dataset pose a problem. The employed tool set for streamlining the measurement process is made publicly available.

Introduction new associate editors

Molecular dynamics simulations of proteins play a growing role in various fields such as pharmaceutical, biochemical and medical research. Accordingly, the need for high quality visualization of these protein systems raises. Highly interactive visualization techniques are especially needed for the analysis of time-dependent molecular simulations. Beside various other molecular representations the surface representations are of high importance for these applications. So far, users had to accept a trade-off between rendering quality and performance--particularly when visualizing trajectories of time-dependent protein data. We present a new approach for visualizing the Solvent Excluded Surface of proteins using a GPU ray casting technique and thus achieving interactive frame rates even for long protein trajectories where conventional methods based on precomputation are not applicable. Furthermore, we propose a semantic simplification of the raw protein data to reduce the visual complexity of the surface and thereby accelerate the rendering without impeding perception of the protein's basic shape. We also demonstrate the application of our Solvent Excluded Surface method to visualize the spatial probability density for the protein atoms over the whole period of the trajectory in one frame, providing a qualitative analysis of the protein flexibility.

We present a development environment for distributed GPU computing targeted for multi-GPU systems, as well as graphics clusters. Our system is based on CUDA and logically extends its parallel programming model for graphics processors to higher levels of parallelism, namely, the PCI bus and network interconnects. While the extended API mimics the full function set of current graphics hardware-including the concept of global memory-on all distribution layers, the underlying communication mechanisms are handled transparently for the application developer. To allow for high scalability, in particular for network-interconnected environments, we introduce an automatic GPU-accelerated scheduling mechanism that is aware of data locality. This way, the overall amount of transmitted data can be heavily reduced, which leads to better GPU utilization and faster execution. We evaluate the performance and scalability of our system for bus and especially network-level parallelism on typical multi-GPU systems and graphics clusters.

During the last 20 years visualization has developed into a scientific discipline of its own right at the crossroads of computer graphics, human-computer interaction, and data analysis. Today, interactive visualization techniques play a crucial role in the process of understanding the huge datasets resulting from simulations, sensor measurements, and information systems. The talk will look at the current state of the field which is characterized by the fact that by now elaborate algorithms and efficient GPU implementations exist for many classical visualization problems dealing with 3D scalar and vector data sets. Therefore, it has become obvious that future visualization research cannot only concentrate on optimizing standard techniques for well-known benchmark data sets. A closer cooperation with application domains very often leads to real-life problems whose size and dimension require new approaches often combining known techniques into innovative tools. In this context the talk will present some of the current research results of the visualization group at the University of Stuttgart in the areas of visualization of video streams, molecular dynamics simulations, and patent databases.

Volume rendering of multiple intersecting volumetric objects is a difficult visualization task, especially if different rendering styles need to be applied to the components, in order to achieve the desired illustration effect. Real-time performance for even complex scenarios is obtained by exploiting the speed and flexibility of modern GPUs, but at the same time programming the necessary shaders turned into a task for GPU experts only. We foresee the demand for an intermediate level of programming abstraction where visualization specialists can realize advanced applications without the need to deal with shader programming intricacies. In this paper, we describe a generic technique for multi-volume rendering, which generates shader code dynamically from an abstract render graph. By combining pre-defined nodes, complex volume operations can be realized. Our system efficiently creates GPU-based fragment shader and vertex shader programs "on-the-fly" to achieve the desired visual results. We demonstrate the flexibility of our technique by applying several dynamically generated volume rendering styles to multi-modal medical datasets.

Future manufacturing processes have to be highly adaptive and transformable in order to support short product life cycles and highly customized products. For an implementation of such a flexible, so-called smart factory, which is driven by a massive proliferation and usage of tiny embedded, context-aware information and communication technologies, many challenges - technical and social ones - have to be addressed. We identify major problems and focus on the location of objects and persons within manufacturing environments as an enabling key technology. Existing methods and possible solutions are evaluated and discussed. Thereby, we focus on the crucial aspect of privacy for workers when location systems are utilized. We also present a semi-automatic positioning approach that solves the discrepancy of guaranteeing the privacy of persons and highly accurate location tracking.

In this paper we present a new approach for using semantic wikis for collaborative patent search and annotation. We describe an extension that allows integrating interactive visualizations into semantic wikis for getting deeper insights into the classificatory, geographical, and temporal distribution of large patent sets. This approach differs from typical wiki usage scenarios in the sense that it combines automatic content generation based on patent search activities of the users with user driven semantic annotation of patent information, e.g. patent rating, linking with prior art, reviews, translations, discussions, etc. The content generation involves a semantic model that is described in terms of different ontologies for patent information. A modern wiki system is used for semantic annotation, comments, discussions, versioning, notification, and full-text search. Our approach is motivated by using available functionalities of a modern wiki system in combination with visualization techniques to directly implement major user requirements for supporting the knowledge-intensive tasks of patent search and understanding.

In this paper we present three software design patterns for rapid prototyping of information visualization applications. The first pattern describes a mapping of object oriented models to relational data tables used in many visualization frameworks. The second pattern describes a script based approach for the configuration of visualization applications. The third pattern addresses the problem of performing online changes on the visual mapping by enhancing fine-grained mapping operators with scripting capabilities. We present an implementation of the patterns, which is based on the Prefuse toolkit. Finally we discuss some experimental results according to performance issues.

Abstract Water is known to play a crucial role in protein structure, flexibility and activity. The use of molecular dynamics simulations allows detailed studies of complex protein‐solvent interactions. Cluster analysis and density‐based approaches have been successfully used for the identification and analysis of conserved water molecules and hydration patterns of proteins. However, appropriate tools for analysing long‐time molecular dynamics simulations with respect to tracking and visualising the paths of solvent molecules are lacking. Our method focuses on visualising the solvent paths entering and leaving cavities of the protein and allows to study the route and dynamics of the exchange of tightly bound internal water molecules with the bulk solvent. The proposed visualisation also represents dynamic properties such as direction and velocity in the solvent. Especially, by clustering similar path‐lines with respect to designated properties the visualisation can be abstracted to represent the principal paths of solvent molecules through the cavities. Its application in the analysis of long‐time scale molecular dynamics simulations not only confirmed conjectures based on previous manual observations made by chance, but also led to novel insights into the dynamical and structural role of water molecules and its interplay with protein structure.

Abstract We propose a novel vortex core line extraction method based on the λ2vortex region criterion in order to improve the detection of vortex features for 3D flow visualization. The core line is defined as a curve that connects λ2minima restricted to planes that are perpendicular to the core line. The basic algorithm consists of the following stages: (1) λ2field construction and isosurface extraction; (2) computation of the curve skeleton of the λ2isosurface to build an initial prediction for the core line; (3) correction of the locations of the prediction by searching for λ2minima on planes perpendicular to the core line. In particular, we consider the topology of the vortex core lines, guaranteeing the same topology as the initial curve skeleton. Furthermore, we propose a geometry‐guided definition of vortex bifurcation, which represents the split of one core line into two parts. Finally, we introduce a user‐guided approach in order to narrow down vortical regions taking into account the graph of λ2along the computed vortex core line. We demonstrate the effectiveness of our method by comparing our results to previous core line detection methods with both simulated and experimental data; in particular, we show robustness of our method for noise‐affected data.

One challenge in video processing is to detect actions and events, known or unknown, in video streams dynamically. This paper proposes a visualization solution, where a video stream is depicted as a series of snapshots at a relatively sparse interval, and detected actions are highlighted with continuous abstract illustrations. The combined imagery and illustrative visualization conveys multi-field information in a manner similar to electrocardiograms (ECG) and seismographs. We thus name this type of video visualization as VideoPerpetuoGram (VPG). In this paper, we describe a system that handles the aw and processed information of the video stream in a multi-field visualization pipeline. As examples, we consider the needs for highlighting several types of processed information, including detected actions in video streams, and estimated relationship between recognized objects. We examine the effective means for depicting multi-field information in VPG, and support our choice of visual mappings through a survey. Our GPU implementation facilitates the VPG-specific viewing specification through a sheared object space, as well as volume bricking and combinational rendering of volume data and glyphs.

We introduce a point-based algorithm for computing and rendering stream surfaces and path surfaces of a 3D flow. The points are generated by particle tracing, and an even distribution of those particles on the surfaces is achieved by selective particle removal and creation. Texture-based surface flow visualization is added to show inner flow structure on those surfaces. We demonstrate that our visualization method is designed for steady and unsteady flow alike: both the path surface component and the texture-based flow representation are capable of processing time-dependent data. Finally, we show that our algorithms lend themselves to an efficient GPU implementation that allows the user to interactively visualize and explore stream surfaces and path surfaces, even when seed curves are modified and even for time-dependent vector fields.

Patents are an invaluable source of scientific and technological information. Due to the strongly increasing number of patent applications and the broadening of the objectives of patent analysis, there is a great demand for ubiquitous access to patent information and for flexible visualizations meeting the requirements of different groups of users. In this paper we propose new visualization techniques for patent information and approaches for interactively exploring this information in web based environments. We show how these visualizations can be integrated into existing web portals by using a new paradigm that we call Semantic Lens.

BECOMING Editor-in-Chief of such an excellent journal as the IEEE Transactions on Visualization and Computer Graphics is a great honor as well as a big challenge for me. I am grateful to the search committee and to the IEEE Computer Society Publications Board for giving me the chance to infl uence and shape the future of one of the most outstanding scientifi c publications in the fi elds of computer graphics, visualization, virtual reality, and haptics. The excellent standing of TVCG is the result of the dedication and hard work by the previous EICs, Arie Kaufmann, Hans Hagen, and David S. Ebert, and it will be challenging to further improve on the high standards set by them. I am sure I can speak on behalf of the Computer Society publications staff, on behalf of the TVCG Editorial Board, and on behalf of the TVCG authors and its entire readership if I express my sincerest gratitude to the outgoing EIC, David S. Ebert. He has put tremendous effort into bringing TVCG into the excellent position it has today. Under his proactive leadership, many important metrics for the journal have improved signifi cantly. He has successfully broadened the scope and helped increase the number of submissions, while maintaining high quality, which led to a constant increase in published papers. His tight monitoring of the entire review process resulted in a major decrease in the time between submission and fi rst decision. Also, he took the lead in integrating the Proceedings of the IEEE Visualization Conference and Symposium on Information Visualization into the journal by establishing a review cycle that meets the high TVCG standards in a very tight time frame. I will surely rely on his support and advice in the future. While TVCG is in great shape, the conditions surrounding it are constantly changing. Our fi eld and our community are both growing, while research funding is decreasing. Researchers are evaluated by counting publications in top journals, while at the same time the growing number of conferences, symposia, and workshops leads to permanent deadline pressure. Electronic dissemination of scientifi c knowledge is becoming standard, while library budgets are shrinking. These new challenges for the journal must be addressed in the future. I rely on you, the readers and authors of TVCG, to share with me your creative ideas to further improve TVCG. The foundation upon which future developments are built is the increasing number of high quality submissions. I encourage you to submit your latest research results to TVCG and to take advantage of the Digital Library’s support for multimedia content. Please spread the word about TVCG’s short decision cycle, the early availability of issues in the Digital Library, and our continuing search for new and interesting topics for special issues.

A current research topic in molecular thermodynamics is the condensation of vapor to liquid and the investigation of this process at the molecular level. Condensation is found in many physical phenomena, e.g. the formation of atmospheric clouds or the processes inside steam turbines, where a detailed knowledge of the dynamics of condensation processes will help to optimize energy efficiency and avoid problems with droplets of macroscopic size. The key properties of these processes are the nucleation rate and the critical cluster size. For the calculation of these properties it is essential to make use of a meaningful definition of molecular clusters, which currently is a not completely resolved issue. In this paper a framework capable of interactively visualizing molecular datasets of such nucleation simulations is presented, with an emphasis on the detected molecular clusters. To check the quality of the results of the cluster detection, our framework introduces the concept of flow groups to highlight potential cluster evolution over time which is not detected by the employed algorithm. To confirm the findings of the visual analysis, we coupled the rendering view with a schematic view of the clusters' evolution. This allows to rapidly assess the quality of the molecular cluster detection algorithm and to identify locations in the simulation data in space as well as in time where the cluster detection fails. Thus, thermodynamics researchers can eliminate weaknesses in their cluster detection algorithms. Several examples for the effective and efficient usage of our tool are presented.

This paper presents an interactive technique for the dense texture-based visualization of unsteady 3D flow, taking into account issues of computational efficiency and visual perception. High efficiency is achieved by a 3D graphics processing unit (GPU)-based texture advection mechanism that implements logical 3D grid structures by physical memory in the form of 2D textures. This approach results in fast read and write access to physical memory, independent of GPU architecture. Slice-based direct volume rendering is used for the final display. We investigate two alternative methods for the volumetric illumination of the result of texture advection: First, gradient-based illumination that employs a real-time computation of gradients, and, second, line-based lighting based on illumination in codimension 2. In addition to the Phong model, perception-guided rendering methods are considered, such as cool/warm shading, halo rendering, or color-based depth cueing. The problems of clutter and occlusion are addressed by supporting a volumetric importance function that enhances features of the flow and reduces visual complexity in less interesting regions. GPU implementation aspects, performance measurements, and a discussion of results are included to demonstrate our visualization approach.

In previous work we have presented a prototype of an assistant system for the blind that can be used for self-localization and interactive object identification of static objects stored within 3D environment models. In this paper we present a new method for interactive tracking of various types of movable objects. The state of fixed movable objects, like doors, can be recognized by comparing the distance between sensor data and a 3D model. For the identification and model-based tracking of free movable objects, like chairs, we have developed an algorithm that is similar to human perception, based on shape and color comparisons to trained objects. Further, using a common face detection algorithm, our assistant system informs the user of the presence of people, and enables the localization of a real person based on interactive tracking of virtual models of humans.

We propose a new approach for the visualization of hyperstreamlines, which offers potential for better scalability than the conventional polygon-based approach. Our method circumvents the bandwidth bottleneck between the CPU and GPU by transmitting a small set of parameters for each tube segment and generates the surface directly on the GPU using the classical sphere tracing approach. This reduces the load on the CPU that would otherwise need to provide a suitable level-of-detail representation of the scene, while offering even higher quality in the resulting surfaces since every fragment is traced individually. We demonstrate the effectiveness of this approach by comparing it to the performance and output of conventional visualization tools in the application area of diffusion tensor imaging of human brain MR scans. The method presented here can also be utilized to generate other types of surfaces on the GPU that are too complex to handle with direct ray casting and can therefore be adapted for other applications.

Point-based models are gaining lately considerable attention as an alternative to traditional surface meshes. In this context, Point Set Surfaces (PSS) were proposed as a modeling and rendering method with important topological and approximation properties. However, ray-tracing PSS is computationally expensive. Therefore, we propose an interactive ray-tracing algorithm for PSS implemented completely on commodity graphics hardware. We also exploit the advantages of PSS to propose a novel technique for extracting surfaces directly from volumetric data. This technique is based on the well known predictor-corrector principle from the numerical methods for solving ordinary differential equations. Our technique provides good approximations to surfaces defined by a certain property in the volume, such as iso-surfaces or surfaces located at regions of high gradient magnitude. Also, local details of the surfaces could be manipulated by changing the local polynomial approximation and the smoothing parameters used. Furthermore, the surfaces generated are smooth and low frequency noise is naturally handled.

Abstract Functional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics, including surface, volume, and vector datasets. Typically, sums of Gaussians or similar radial basis functions are used in the functional approximation and PC graphics hardware is used to quickly evaluate and render these datasets. Previously, researchers presented techniques for spatially‐limited spherical Gaussian radial basis function encoding and visualization of volumetric scalar, vector, and multifield datasets. While truncated radially symmetric basis functions are quick to evaluate and simple for encoding optimization, they are not the most appropriate choice for data that is not radially symmetric and are especially problematic for representing linear, planar, and many non‐spherical structures. Therefore, we have developed a volumetric approximation and visualization system using ellipsoidal Gaussian functions which provides greater compression, and visually more accurate encodings of volumetric scattered datasets. In this paper, we extend previous work to use ellipsoidal Gaussians as basis functions, create a rendering system to adapt these basis functions to graphics hardware rendering, and evaluate the encoding effectiveness and performance for both spherical Gaussians and ellipsoidal Gaussians. Categories and Subject Descriptors (according to ACMCCS): I.3.3 [Computer Graphics]: Scientific Visualization, Ellipsoidal Basis Functions, Functional Approximation, Texture Advection

Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study, and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have compared the effectiveness of different abstract visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It has provided the first set of evidence confirming that ordinary users can be accustomed to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion events.

This paper describes methods for explanatory and illustrative visualizations used to communicate aspects of Einstein's theories of special and general relativity, their geometric structure, and of the related fields of cosmology and astrophysics. Our illustrations target a general audience of laypersons interested in relativity. We discuss visualization strategies, motivated by physics education and the didactics of mathematics, and describe what kind of visualization methods have proven to be useful for different types of media, such as still images in popular science magazines, film contributions to TV shows, oral presentations, or interactive museum installations. Our primary approach is to adopt an egocentric point of view: The recipients of a visualization participate in a visually enriched thought experiment that allows them to experience or explore a relativistic scenario. In addition, we often combine egocentric visualizations with more abstract illustrations based on an outside view in order to provide several presentations of the same phenomenon. Although our visualization tools often build upon existing methods and implementations, the underlying techniques have been improved by several novel technical contributions like image-based special relativistic rendering on GPUs, special relativistic 4D ray tracing for accelerating scene objects, an extension of general relativistic ray tracing to manifolds described by multiple charts, GPU-based interactive visualization of gravitational light deflection, as well as planetary terrain rendering. The usefulness and effectiveness of our visualizations are demonstrated by reporting on experiences with, and feedback from, recipients of visualizations and collaborators.

We present a novel visualization method for mono- and dipolar molecular simulations from thermodynamics that takes advantage of modern graphics hardware to interactively render specifically tailored glyphs. Our approach allows domain experts to visualize the results of molecular dynamics simulations with a higher number of particles than before and furthermore offers much better visual quality. We achieve this by transferring only visualization parameters to the GPU and by generating implicit surfaces directly in the fragment program. As a result, we can render up to 500.000 glyphs with about 10 fps displaying all the simulation results as geometrical properties that resemble the classical abstract representation used in this research area. Thus we enable researchers to visually assess the results of simulations of greater scale than before. We believe that the proposed method can be generalized to create other kinds of parametrized surfaces directly on graphics hardware to overcome the bandwidth bottleneck that exists between CPU and GPU.

This paper presents an interactive technique for the dense texture-based visualization of unsteady 3D flow, taking into account issues of computational efficiency and visual perception. High efficiency is achieved by a novel 3D GPU-based texture advection mechanism that implements logical 3D grid structures by physical memory in the form of 2D textures. This approach results in fast read and write access to physical memory, independent of GPU architecture. Slice-based direct volume rendering is used for the final display. A real-time computation of gradients is employed to achieve volume illumination. Perception-guided volume shading methods are included, such as halos, cool/warm shading, or color-based depth cueing. The problems of clutter and occlusion are addressed by supporting a volumetric importance function that enhances features of the flow and reduces visual complexity in less interesting regions.

In this paper, we present two novel texture-based techniques to visualize uncertainty in time-dependent 2D flow fields. Both methods use semi-Lagrangian texture advection to show flow direction by streaklines and convey uncertainty by blurring these streaklines. The first approach applies a cross advection perpendicular to the flow direction. The second method employs isotropic diffusion that can be implemented by Gaussian filtering. Both methods are derived from a generic filtering process that is incorporated into the traditional texture advection pipeline. Our visualization methods allow for a continuous change of the density of flow representation by adapting the density of particle injection. All methods can be mapped to efficient GPU implementations. Therefore, the user can interactively control all important characteristics of the system like particle density, error influence, or dye injection to create meaningful illustrations of the underlying uncertainty. Even though there are many sources of uncertainties, we focus on uncertainty that occurs during data acquisition. We demonstrate the usefulness of our methods for the example of real-world fluid flow data measured with the particle image velocimetry (PIV) technique. Furthermore, we compare these techniques with an adapted multi-frequency noise approach.

GPU-based raycasting offers an interesting alternative to conventional slice-based volume rendering due to the inherent flexibility and the high quality of the generated images. Recent advances in graphics hardware allow for the ray traversal and volume sampling to be executed on a per-fragment level completely on the GPU leading to interactive framerates. In this work we present optimization techniques that improve the performance and quality of GPU-based volume raycasting. We apply a hybrid image/object space approach to accelerate the ray traversal in animation sequences that works for both isosurface rendering and semi-transparent volume rendering. An empty-space-leaping technique that exploits the spatial coherence between consecutively rendered images is used to estimate the optimal initial ray sampling point for each image pixel. These can double the rendering performance for typical volumetric data sets without sacrificing image quality. The achieved speed-up allows for further improvements of image quality. We demonstrate an object space antialiasing technique based on selective super-sampling at sharp creases and silhouette edges which also benefits from exploiting frame-to-frame coherence.

Gaining a comprehensive understanding of turbulent flows still poses one of the great challenges in fluid dynamics. A well-established approach to advance this research is the analysis of the vortex structures contained in the flow. In order to be able to perform this analysis efficiently, supporting visualization tools with clearly defined requirements are needed. In this paper, we present a visualization system which matches these requirements to a large extent. The system consists of two components. The first component analyzes the flow by means of a novel combination of vortex core line detection and the /spl lambda//sub 2/ method. The second component is a vortex browser which allows for an interactive exploration and manipulation of the vortices detected and separated during the first phase. Our system improves the reliability and applicability of existing vortex detection methods and allows for a more efficient study of vortical flows which is demonstrated in an evaluation performed by experts.

In this application paper, we report on over fifteen years of experience with relativistic and astrophysical visualization, which has been culminating in a substantial engagement for visualization in the Einstein Year 2005 - the 100/sup th/ anniversary of Einstein's publications on special relativity, the photoelectric effect, and Brownian motion. This paper focuses on explanatory and illustrative visualizations used to communicate aspects of the difficult theories of special and general relativity, their geometric structure, and of the related fields of cosmology and astrophysics. We discuss visualization strategies, motivated by physics education and didactics of mathematics, and describe what kind of visualization methods have proven to be useful for different types of media, such as still images in popular-science magazines, film contributions to TV shows, oral presentations, or interactive museum installations. Although our visualization tools build upon existing methods and implementations, these techniques have been improved by several novel technical contributions like image-based special relativistic rendering on GPUs, an extension of general relativistic ray tracing to manifolds described by multiple charts, GPU-based interactive visualization of gravitational light deflection, as well as planetary terrain rendering. The usefulness and effectiveness of our visualizations are demonstrated by reporting on experiences with, and feedback from, recipients of visualizations and collaborators.

We propose a hybrid particle and texture based approach for the visualization of time-dependent vector fields. The underlying space-time framework builds a dense vector field representation in a two-step process: 1) particle-based forward integration of trajectories in spacetime for temporal coherence, and 2) texture-based convolution along another set of paths through the spacetime for spatially correlated patterns. Particle density is controlled by stochastically injecting and removing particles, taking into account the divergence of the vector field. Alternatively, a uniform density can be maintained by placing exactly one particle in each cell of a uniform grid, which leads to particle-in-cell forward advection. Moreover, we discuss strategies of previous visualization methods for unsteady flow and show how they address issues of spatiotemporal coherence and dense visual representations. We demonstrate how our framework is capable of realizing several of these strategies. Finally, we present an efficient GPU implementation that facilitates an interactive visualization of unsteady 2D flow on Shader Model 3 compliant graphics hardware.

In this paper we present a new system that assists blind users in orienting themselves in indoor environments. We developed a sensor module that can be handled like a flashlight by a blind user and can be used for searching tasks within the three-dimensional environment. By pressing keys, inquiries concerning object characteristics, position, orientation and navigation can be sent to a connected portable computer, or to a federation of data servers providing models of the environment. Finally these inquiries are acoustically answered over a text-to-speech engine.

There is a growing interest for providing interactive graphics also on mobile devices like PDAs, Smartphones, etc. Right now mobile devices are very limited concerning graphical resources basically only simple 2D rasterization operations are available, that run completely on the main CPU of the device. The desire to have more complex 3D graphics on these devices often results in a remote rendering solution. Classical remote rendering solutions produce the final images on a server and transfer these to the client device over a wired or nonwired network. We present an alternative method to the image-based approach by splitting the rendering process between client and server transfering couple of 2D line primitives over the network, which are rendered locally by the mobile device

3D texture-based volume rendering is a popular way of realizing direct volume visualization on graphics hardware. However, the slice-oriented texture memory layout of many current CPUs may lead to a strongly view-dependent performance, which reduces the fields of application of volume rendering. In this short technical note, we propose a slight modification of texture-based volume rendering that maintains roughly constant frame rates on any GPU architecture. The idea is to split the volume into smaller sub-volumes. These bricks can be oriented in different directions; thus the varying performance for different viewing directions is averaged out

Volume visualization using isosurface extraction is a well-researched topic. Research demonstrated that even for unstructured grids peak performances of millions of tetrahedra per second can be achieved by exploiting the parallel processing capabilities of modern GPUs. In this paper we present a hardware-accelerated solution that further improves the extraction performance. In contrary to existing approaches, our technique explicitly extracts the isosurface geometry in a fragment program by rendering only a single screen-sized quadrilateral. The extracted geometry is directly written to an on-board graphics memory object allowing for direct rendering without further bus transfers. Additionally, the geometry can be manipulated by shader programs or read back to the application for further processing. Examples and application scenarios are given that can benefit from our approach.

Virtual prototyping is increasingly replacing real mock-ups and experiments in industrial product development. Part of this process is the simulation of structural and functional properties, which is in many cases based on finite element analysis (FEA). One prominent example from the automotive industry is the safety improvement resulting from crash worthiness simulations. A simulation model for this purpose usually consists of up to one million finite elements and is assembled from many parts, which are individually meshed out of their CAD representation. In order to accelerate the development cycle, simulation engineers want to be able to modify their FE models without going back to the CAD department. Furthermore, valid CAD models might even not be available in preliminary design stages. However, in contrast to CAD, there is a lack of tools that offer the possibility of modification and processing of finite element components while maintaining the properties relevant to the simulation. In this application paper we present interactive algorithms for intuitive and fast editing of FE models and appropriate visualization techniques to support engineers in understating these models. This includes new kinds of manipulators, feedback mechanisms and facilities for virtual reality and immersion at the workplace, e.g. autostereoscopic displays and haptic devices.

Feature detection in flow fields is a well-researched area, but practical application is often difficult due to the numerical complexity of the algorithms preventing interactive use and due to noise in experimental or high-resolution simulation data sets. We present an integrated system that provides interactive denoising, vortex detection, and visualisation of vector data on Cartesian grids. All three major phases are implemented in such a way that the system runs completely on a modern GPU once the vector field is downloaded into graphics memory. The application aspect of our paper is twofold. First, we show how recently presented, prototypical GPU-based algorithms for filtering, numerical computation, and volume rendering can be combined into one productive system by handling all idiosyncrasies of a chosen graphics card. Second, we demonstrate that the significant speedup achieved compared to an optimized software implementation now allows interactive exploration of characteristic structures in turbulent flow fields.

Noise reduction is an important preprocessing step for many visualization techniques that make use of feature extraction. We propose a method for denoising 2-D vector fields that are corrupted by additive noise. The method is based on the vector wavelet transform and wavelet coefficient thresholding. We compare our wavelet-based denoising method with Gaussian filtering, and test the effect of these methods on the signal-to-noise ratio (SNR) of the vector fields before and after denoising. We also study the effect on relevant details for visualization, such as vortex measures. The results show that for low SNR, Gaussian filtering with large kernels has a somewhat higher performance than the wavelet-based method in terms of SNR. For larger SNR, the wavelet-based method outperforms Gaussian filtering. This is mostly due to the fact that Gaussian filtering tends to remove small details, which are preserved by the wavelet-based method.

Abstract In this paper, we present a mapping of nonlinear ray tracing to the GPU which avoids any data transfer back to main memory. The rendering process consists of the following parts: ray setup according to the camera parameters, ray integration, ray‐object intersection, and local illumination. Bent rays are approximated by polygonal lines that are represented by textures. Ray integration is based on an iterative numerical solution of ordinary differential equations whose initial values are determined during ray setup. To improve the rendering performance, we propose acceleration techniques such as early ray termination and adaptive ray integration. Finally, we discuss a variety of applications that range from the visualization of dynamical systems to the general relativistic visualization in astrophysics and the rendering of the continuous refraction in media with varying density. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism

Mesh simplification algorithms play an important role in computer graphics. In particular, view-dependent simplification methods are utilized widely to reduce the geometric complexity of large outdoor scenes. The so called continuous level of detail technique, for instance, is prevalent in the area of terrain rendering. We apply the latter technique to the more general volumetric case. We propose a volume rendering algorithm, which constructs a hierarchical tetrahedral mesh from regular volume data in a view-dependent fashion. The popping effect which generally arises from view-dependent algorithms is addressed by a new fast method for the smooth interpolation of the mesh hierarchy. We demonstrate the performance of our algorithm by displaying clouds in real-time. Here, the application of the preintegration technique allows for a wide range of cloud appearance and guarantees accurate compositing. Additionally, we show how to utilize our algorithm for the accelerated display of ground fog in terrain rendering scenarios.

Numerical particle simulations and astronomical observations create huge data sets containing uncorrelated 3D points of varying size. These data sets cannot be visualized interactively by simply rendering millions of colored points for each frame. Therefore, in many visualization applications a scalar density corresponding to the point distribution is resampled on a regular grid for direct volume rendering. However, many fine details are usually lost for voxel resolutions which still allow interactive visualization on standard workstations. Since no surface geometry is associated with our data sets, the recently introduced point-based rendering algorithms cannot be applied as well. In this paper we propose to accelerate the visualization of scattered point data by a hierarchical data structure based on a PCA clustering procedure. By traversing this structure for each frame we can trade-off rendering speed vs. image quality. Our scheme also reduces memory consumption by using quantized relative coordinates and it allows for fast sorting of semi-transparent clusters. We analyze various software and hardware implementations of our renderer and demonstrate that we can now visualize data sets with tens of millions of points interactively with sub-pixel screen space error on current PC graphics hardware employing advanced vertex shader functionality.

We present the first implementation of a volume ray casting algorithm for tetrahedral meshes running on off-the-shelf programmable graphics hardware. Our implementation avoids the memory transfer bottleneck of the graphics bus since the complete mesh data is stored in the local memory of the graphics adapter and all computations, in particular ray traversal and ray integration, are performed by the graphics processing unit. Analogously to other ray casting algorithms, our algorithm does not require an expensive cell sorting. Provided that the graphics adapter offers enough texture memory, our implementation performs comparable to the fastest published volume rendering algorithms for unstructured meshes. Our approach works with cyclic and/or non-convex meshes and supports early ray termination. Accurate ray integration is guaranteed by applying pre-integrated volume rendering. In order to achieve almost interactive modifications of transfer functions, we propose a new method for computing three-dimensional pre-integration tables.

We propose unsteady flow advection-convolution (UFAC) as a novel visualization approach for unsteady flows. It performs time evolution governed by pathlines, but builds spatial correlation according to instantaneous streamlines whose spatial extent is controlled by the flow unsteadiness. UFAC is derived from a generic framework that provides spacetime-coherent dense representations of time dependent-vector fields by a two-step process: 1) construction of continuous trajectories in spacetime for temporal coherence; and 2) convolution along another set of paths through the above spacetime for spatially correlated patterns. Within the framework, known visualization techniques-such as Lagrangian-Eulerian advection, image-based flow visualization, unsteady flow LIC, and dynamic LIC-can be reproduced, often with better image quality, higher performance, or increased flexibility of the visualization style. Finally, we present a texture-based discretization of the framework and its interactive implementation on graphics hardware, which allows the user to gradually balance visualization speed against quality.

Abstract In this paper we discuss different approaches to generate cutaway illustrations. The purpose of such a drawingis to allow the viewer to have a look into an otherwise solid opaque object. Traditional methods to draw thesekinds of illustrations are evaluated to extract a small and effective set of rules for a computer‐based renderingof cutaway illustrations. We show that our approaches are not limited to a specific rendering style but can besuccessfully combined with a great variety of well‐known artistic or technical illustration techniques. All methodsof this paper make use of modern graphics hardware functionality to achieve interactive frame rates.

We present two implementations of a view-independent cell projection algorithm for off-the-shelf programmable graphics hardware. Both implementations perform all computations for the projection and scan conversion of a set of tetrahedra on the graphics hardware and are therefore compatible with many of the hardware-accelerated optimizations for polygonal graphics, e.g., OpenGL vertex arrays and display lists. Apart from our actual implementations, we discuss potential improvements on future, more flexible graphics hardware and applications to interactive volume visualization of unstructured meshes.

We propose clipping methods that are capable of using complex geometries for volume clipping. The clipping tests exploit per-fragment operations on the graphics hardware to achieve high frame rates. In combination with texture-based volume rendering, these techniques enable the user to interactively select and explore regions of the data set. We present depth-based clipping techniques that analyze the depth structure of the boundary representation of the clip geometry to decide which parts of the volume have to be clipped. In another approach, a voxelized clip object is used to identify the clipped regions. Furthermore, the combination of volume clipping and volume shading is considered. An optical model is introduced to merge aspects of surface-based and volume-based illumination in order to achieve a consistent shading of the clipping surface. It is demonstrated how this model can be efficiently incorporated in the aforementioned clipping techniques.

Active stereo has been used by engineers and industrial designers for several years to enhance the perception of computer generated three-dimensional images. Unfortunately, active stereo requires specialized hardware. Therefore, as ubiquitous computing and teleworking gain importance, using active stereo becomes a problem. The goal of this case study is to examine the concept of a generic library for polychromatic passive stereo to make stereo vision available everywhere.

We propose new clipping methods that are capable of using complex geometries for volume clipping. The clipping tests exploit per-fragment operations on the graphics hardware to achieve high frame rates. In combination with texture-based volume rendering, these techniques enable the user to interactively select and explore regions of the data set. We present depth-based clipping techniques that analyze the depth structure of the boundary representation of the clip geometry to decide which parts of the volume have to be clipped. In another approach, a voxelized clip object is used to identify the clipped regions.

This paper describes how technical illustrations containing opaque and non-opaque objects can be automatically generated. Traditional methods to show transparency in manual drawings are evaluated to extract a small and effective set of rules for computer-based rendering of technical illustrations, leading to a novel view-dependent transparency model. We propose a hardware-accelerated depth sorting algorithm in image-space which specifically meets the requirements of our transparency model. In this way, real-time rendering of semi-transparent technical illustrations is achieved. Finally, it is described how our approach can be combined with other methods in the field of non-photorealistic rendering in order to enhance the visual perception of technical illustrations. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Color, shading, shadowing and texture

One of the main scientific goals of visualization is the development of algorithms and appropriate data models which facilitate interactive visual analysis and direct manipulation of the increasingly large data sets which result from simulations running on massive parallel computer systems, from measurements employing fast high-resolution sensors, or from large databases and hierarchical information spaces. This task can only be achieved with the optimization of all stages of the visualization pipeline: filtering, compression, and feature extraction of the raw data sets, adaptive visualization mappings which allow the users to choose between speed and accuracy, and exploiting new graphics hardware features for fast and high-quality rendering. The recent introduction of advanced programmability in widely available graphics hardware has already led to impressive progress in the area of volume visualization. However, besides the acceleration of the final rendering, flexible graphics hardware is increasingly being used also for the mapping and filtering stages of the visualization pipeline, thus giving rise to new levels of interactivity in visualization applications. The talk will present recent results of applying programmable graphics hardware in various visualization algorithms covering volume data, flow data, terrains, NPR rendering, and distributed and remote applications.

The paper describes an architecture that enables the use of commodity off the shelf graphics hardware along with high speed network devices for distributed volume rendering. Several PCs drive a number of graphic accelerator boards using an OpenGL interface. The frame buffers of the cards are read back and blended together for final presentation on a single PC working as front end. We explain how the attainable frame rates are limited by the transfer speeds over the network as well as the overhead implied by having to blend several images together. An initial implementation using four graphic cards achieves frame rates similar to those of high performance visualization systems.

The case study presents a medical Web service for the automatic analysis of CTA (computer tomography angiography) datasets. It aims at the detection and evaluation of intracranial aneurysms which are malformations of cerebral blood vessels. To obtain a standardized 3D visualization, digital videos are automatically generated. The time-consuming video production caused by the manual delineation of structures, software based volume rendering, and the interactive definition of an optimized camera path is considerably improved with a fully automatic strategy. Therefore, a previously suggested approach (C. Rezk-Salama, 2000) is applied which uses an optimized transfer function as a template and automatically adapts it to an individual dataset. Furthermore, we introduce hardware-accelerated morphologic filtering in order to detect the location of mid-size and giant aneurysms. The actual generation of the video is finally integrated into a hardware accelerated off-screen rendering process based on 3D texture mapping, ensuring fast visualization of high quality. Overall, clinical routine can be considerably assisted by providing a Web based service combining automatic detection and standardized visualization.

We present the first algorithm that employs hardware-accelerated cell-projection for direct volume rendering of cyclic meshes, i.e., meshes with visibility cycles. The visibility sorting of a cyclic mesh is performed by an extended topological sorting, which computes and isolates visibility cycles. Measured sorting times are comparable to previously published algorithms, which are, however, restricted to acyclic meshes. In practice, our algorithm is also useful for acyclic meshes as numerical instabilities can lead to false visibility cycles. Our method includes a simple, hardware-assisted algorithm based on image compositing that renders visibility cycles correctly. For tetrahedral meshes this algorithm allows us to render each tetrahedral cell (whether it is part of a cycle or not) by hardware-accelerated cell-projection. In its basic form our method applies only to convex cyclic meshes; however, we present an exact and a simpler but inexact extension of our method for nonconvex meshes.

In this paper we address the problem of interactively resampling unstructured grids. Three algorithms are presented. They all allow adaptive resampling of an unstructured grid on a multiresolution hierarchy of arbitrarily sized cartesian grids according to a varying element size. Two of the algorithms presented take advantage of hardware accelerated polygon rendering and 2D texture mapping. In exploiting new features of modem PC graphics adapters, the first algorithm tries to significantly minimize the number of polygons to be rendered. Reducing rasterization requirements is the main goal of the second algorithm, which distributes the computational workload differently between the main processor and the graphics chip. By comparing them to a new pure software approach, an optimal software-hardware balance is studied. We end up with a hybrid approach which greatly improves the performance of hardware assisted resampling by involving the main processor to a higher degree and thus enabling resampling at nearly interactive rates.

Proposes a technique for topology-preserving smoothing of sampled vector fields. The vector field data is first converted into a scalar representation in which time surfaces implicitly exist as level sets. We then locally analyze the dynamic behavior of the level sets by placing geometric primitives in the scalar field and by subsequently distorting these primitives with respect to local variations in this field. From the distorted primitives, we calculate the curvature normal and we use the normal magnitude and its direction to separate distinct flow features. Geometrical and topological considerations are then combined to successively smooth dense flow fields, at the same time retaining their topological structure.

For a comprehensive understanding of tomographic image data in medicine, interactive and high-quality direct volume rendering is an essential prerequisite. This is provided by visualization using 3D texture mapping which is still limited to high-end graphics hardware. In order to make it available in a clinical environment, we present a system which uniquely combines local desktop computers and remote high-end graphics hardware. In this context, we exploit the standard visualization capabilities to a maximum which are available in the clinical environment. For 3D representations of high resolution and quality we access the remote specialized hardware. Various tools for 2D and 3D visualization are provided which meet the requirements of a medical diagnosis. This is demonstrated with examples from the field of neuroradiology which show the value of our strategy in practice.

We present two beneficial rendering extensions to the projected tetrahedra (PT) algorithm proposed by Shirley and Tuchman (1990). These extensions are compatible with any cell sorting technique, for example the BSP-XMPVO sorting algorithm for unstructured meshes. Using 3D texture mapping our first extension solves the longstanding problem of hardware-accelerated but accurate rendering of tetrahedral volume cells with arbitrary transfer functions. By employing 2D texture mapping our second extension realizes the hardware-accelerated rendering of multiple shaded isosurfaces within the PT algorithm without reconstructing the isosurfaces. Additionally, two methods are presented to combine projected tetrahedral volumes with isosurfaces. The time complexity of all our algorithms is linear in the number of tetrahedra and does neither depend on the number of isosurfaces nor on the employed transfer functions.

We propose a technique for visualizing steady flow. Using this technique, we first convert the vector field data into a scalar level-set representation. We then analyze the dynamic behavior and subsequent distortion of level-sets and interactively monitor the evolving structures by means of texture-based surface rendering. Next, we combine geometrical and topological considerations to derive a multiscale representation and to implement a method for the automatic placement of a sparse set of graphical primitives depicting homogeneous streams in the fields. Using the resulting algorithms, we have built a visualization system that enables us to effectively display the flow direction and its dynamics even for dense 3D fields.

With fast 3D graphics becoming more and more available even on low end platforms, the focus in developing new graphics hardware is beginning to shift towards higher quality rendering and additional functionality instead of simply higher performance implementations of the traditional graphics pipeline.On this search for improved quality it is important to identify a powerful set of orthogonal features to'be implemented in hardware, which can then be flexibly combined to form new algorithms.Pixel textures are an OpenGL extension by Silicon Graphics that fits into this category.In this paper, we demonstrate the benefits of this extension by presenting several different algorithms exploiting its functionality to achieve high quality, high performance solutions for a variety of different applications from scientific visualization and realistic image synthesis.We conclude that pixel textures are a valuable, powerful feature that should become a standard in future graphics systems.

The reconstruction of isosurfaces from scalar volume data has positioned itself as a fundamental visualization technique in many different applications. But the dramatically increasing size of volumetric data sets often prohibits the handling of these models on affordable low-end single processor architectures. Distributed client-server systems integrating high-bandwidth transmission channels and Web based visualization tools are one alternative to attack this particular problem, but therefore new approaches to reduce the load of numerical processing and the number of generated primitives are required. We outline different scenarios for distributed isosurface reconstruction from large scale volumetric data sets. We demonstrate how to directly generate stripped surface representations and we introduce adaptive and hierarchical concepts to minimize the number of vertices that have to be reconstructed, transmitted and rendered. Furthermore, we propose a novel computation scheme, which allows the user to flexibly exploit locally available resources. The proposed algorithms have been merged together in order to build a platform-independent Web based application. Extensive use of VRML and Java OpenGL bindings allows for the exploration of large scale volume data quite efficiently.

Many volume filtering operations used for image enhancement, data processing or feature detection can be written in terms of three-dimensional convolutions. It is not possible to yield interactive frame rates on todays hardware when applying such convolutions on volume data using software filter routines. As modern graphics workstations have the ability to render two-dimensional convoluted images to the frame buffer, this feature can be used to accelerate the process significantly. This way generic 3D convolution can be added as a powerful tool in interactive volume visualization toolkits.

Line integral convolution (LIC) is an effective technique for visualizing vector fields. The application of LIC to 3D flow fields has yet been limited by difficulties to efficiently display and animate the resulting 3D-images. Texture-based volume rendering allows interactive visualization and manipulation of 3D-LIC textures. In order to ensure the comprehensive and convenient exploration of flow fields, we suggest interactive functionality including transfer functions and different clipping mechanisms. Thereby, we efficiently substitute the calculation of LIC based on sparse noise textures and show the convenient visual access of interior structures. Further on, we introduce two approaches for animating static 3D-flow fields without the computational expense and the immense memory requirements for pre-computed 3D-textures and without loss of interactivity. This is achieved by using a single 3D-LIC texture and a set of time surfaces as clipping geometries. In our first approach we use the clipping geometry to pre-compute a special 3D-LIC texture that can be animated by time-dependent color tables. Our second approach uses time volumes to actually clip the 3D-LIC volume interactively during rasterization. Additionally, several examples demonstrate the value of our strategy in practice.

The work presents interactive flow visualization techniques specifically adapted for PowerFLOW/sup TM/, a lattice based CFD code from the EXA corporation. Their Digital Physics/sup TM/ fluid simulation technique is performed on a hierarchy of locally refined cartesian grids with a fine voxel resolution in areas of interesting flow features. Among other applications, the PowerFLOW solver is used for aerodynamic simulations in car body development where the advantages of automatic grid generation from CAD models is of great interest. In a joint project with BMW and EXA, we are developing a visualization tool which incorporates virtual reality techniques for the interactive exploration of the large scalar and vector data sets. We describe the specific data structures and interpolation techniques and we report on fast particle tracing, taking into account collisions with the car body geometry. An OpenGL Optimizer based implementation allows for the inspection of the flow with particle probes and slice probes at interactive frame rates.

Different imaging modalities give insight to vascular, anatomical and functional information which assist diagnosis and therapy planning in medicine. Registration and consecutive visualization allow to combine the image data and thereby convey more meaningful images to the clinician. Applying a voxel based approach based on mutual information, accurate and retrospective registration is provided. However, optimization and consecutive visualization procedures require a huge amount of trilinear interpolation operations to re-sample the data. Ensuring fast performance which is fundamental for medical routine, we suggest an integrated approach which takes advantage of the imaging and texture mapping subsystem of graphics computers. All trilinear interpolation is completely performed with hardware assisted 3D texture mapping. The 1D and 2D histograms of the datasets which are necessary for the calculation of mutual information are obtained with different hardware accelerated imaging operations. For the simultaneous and interactive visualization of the registered datasets a new approach was developed which allows for versatile fusion operations. Using similar procedures supported by hardware, contributes considerably to accelerate registration and visualization. Implementing our approach within a previously presented framework (Hastreiter et al., 1996) (Sommer et al., 1998) based on OpenInventor and OpenGL provides intuitive manipulation. Clinical examples show the value of our approach in practice.

The authors present an indirect volume visualization method, based on the deformable surface model, which is a three dimensional extension of the snake segmentation method. In contrast to classical indirect volume visualization methods, this model is not based on iso-values but on boundary information. Physically speaking it simulates a combination of a thin plate and a rubber skin, that is influenced by forces implied by feature information extracted from the given data set. The approach proves to be appropriate for data sets that represent a collection of objects separated by distinct boundaries. These kind of data sets often occur in medical and technical tomography, as they demonstrate by a few examples. They propose a multilevel adaptive finite difference solver which generates a target surface minimizing an energy functional based on an internal energy of the surface and an outer energy induced by the gradient of the volume. This functional tends to produce very regular triangular meshes compared to results of the marching cubes algorithm. It makes this method attractive for meshing in numerical simulation or texture mapping. Red-green triangulation allows an adaptive refinement of the mesh. Special considerations have been made to prevent self inter-penetration of the surfaces.

OpenGL and its extensions provide access to advanced per-pixel operations available in the rasterization stage and in the frame buffer hardware of modern graphics workstations.With these mechanisms, completely new rendering algorithms can be designed and implemented in a very particular way.In this paper we extend the idea of extensively using graphics hardware for the rendering of volumetric data sets in various ways.First, we introduce the concept of clipping geometries by means of stencil buffer operations, and we exploit pixel textures for the mapping of volume data to spherical domains.We show ways to use 3D textures for the rendering of lighted and shaded iso-surfaces in real-time without extracting any polygonal representation.Second, we demonstrate that even for volume data on unstructured grids, where only software solutions exist up to now, both methods, iso-surface extraction and direct volume rendering, can be accelerated to new rates of interactivity by simple polygon drawing and frame buffer operations.

One common problem in the practical application of volume visualization is the proper choice of transfer functions in order to color different parts of the volume meaningfully. This interactive process can be very complicated and time consuming. An alternative to the adjustment of transfer functions is the application of segmentation algorithms. These algorithms are often dedicated to a limited range of data sets and tend to be very compute intensive. We propose a morphology based hierarchical analysis to estimate the optical properties of the volume to be rendered. This approach requires fewer parameters and incorporates also spatial information, but it is far less compute intensive than most of the segmentation methods. The hierarchical analysis is constructed in analogy to the wavelet analysis, except for the fact, that nonlinear filters are used in our case. These morphological operators have a lower distortional influence on the analyzed structures than the usual linear filters. A special decomposition of the morphological operators is discussed, which leads to an efficient implementation of this approach. This technique reduces the three dimensional analysis to a one dimensional computation, as it is done in tensor product based linear filters. The resulting decomposition may also be parallelized easily. We demonstrate the usefulness of the proposed technique by applying it to medical and technical data sets.

A multiresolution data decomposition offers a fundamental framework supporting compression, progressive transmission, and level‐of‐detail (LOD) control for large two or three dimensional data sets discretized on complex meshes. In this paper we extend a previously presented algorithm for 3D mesh reduction for volume data based on multilevel finite element approximations in two ways. First, we present efficient data structures which allow to incrementally construct approximations of the volume data at lower or higher resolutions at interactive rates. An abstract description of the mesh hierarchy in terms of a coarse base mesh and a set of integer records offers a high compression potential which is essential for an efficient storage and a progressive network transmission. Based on this mesh hierarchy we then develop a new progressive iso‐surface extraction algorithm. For a given iso‐value, the corresponding iso‐surface can be computed at different levels of resolution. Changing to a higher or coarser resolution will update the surface only in those regions where the volume data is being refined or coarsened. Our approach allows to interactively visualize very large scalar fields like medical data sets, whereas the conventional algorithms would have required at least an order of magnitude more resources.

Multilevel representations and mesh reduction techniques have been used for accelerating the processing and the rendering of large datasets representing scalar- or vector-valued functions defined on complex 2D or 3D meshes. We present a method based on finite element approximations which combines these two approaches in a new and unique way that is conceptually simple and theoretically sound. The main idea is to consider mesh reduction as an approximation problem in appropriate finite element spaces. Starting with a very coarse triangulation of the functional domain, a hierarchy of highly non-uniform tetrahedral (or triangular in 2D) meshes is generated adaptively by local refinement. This process is driven by controlling the local error of the piecewise linear finite element approximation of the function on each mesh element. A reliable and efficient computation of the global approximation error and a multilevel preconditioned conjugate gradient solver are the key components of the implementation. In order to analyze the properties and advantages of the adaptively generated tetrahedral meshes, we implemented two volume visualization algorithms: an iso-surface extractor and a ray-caster. Both algorithms, while conceptually simple, show significant speedups over conventional methods delivering comparable rendering quality from adaptively compressed datasets.

Numerical finite element simulations of the behaviour of a car body in frontal, side or rear impact collision scenarios have become increasingly complex as well as reliable and precise. They are well-established as a standard evaluation tool in the automotive development process. Both the increased complexity and the advances in computer graphics technology have resulted in the need for new visualization techniques to facilitate the analysis of the immense amount of data originating from such scientific engineering computations. Expanding the effectiveness of traditional post-processing techniques is one key to achieve shorter design cycles and faster time-to-market. In this paper, we describe how the extensive use of texture mapping and new visualization mappings like force tubing can considerably enhance the post-processing of structural and physical properties of car components in crash simulations. We show that, using these techniques, both the calculation costs and the rendering costs are reduced, and the quality of the visualization is improved.

A number of techniques have been proposed for rendering volumetric scalar data sets. Techniques have also been proposed for analyzing the three dimensional information contents of the underlying domain, but traditionally the data analysis part is left as a post‐processing step which only involves the rendered two dimensional images. In this paper, we describe a visualization method for scalar volume data which integrates explicit knowledge of the underlying domain into the rendering process. The key of this approach lies in a hierarchical description of the discrete signal, which is decomposed into a sequence of multiscale representations. We describe a technique for the analysis of structures within the data. This allows for the segmentation and classification of the relevant features and can be used to improve their visual sensation. We also address the problem of accelerating the final rendering pass by integrating the extracted object space information into the ray traversal process.

This paper reports on progress we have made in modelling cosmic X-ray sources on supercomputers. The results we present are meant to serve as an example for the fact that sophisticated visualization techniques play a crucial role in scientific computing. Among the graphical methods we demonstrate, raytracing in curved space-time and a physically motivated 3D-volume rendering algorithm might be of interest to the graphics community in general.