Public-facing data visualizations can play a vital role in making complex information clear and engaging, thereby encouraging informed public discourse and participation. However, existing work offers limited insight into how practitioners make design decisions based on their envisioned target audiences and across different media channels. To investigate this, we conducted semi-structured interviews with 21 professionals from journalistic settings, focusing on how they conceptualize their readers, translate these notions into design choices, and evaluate their work. We found that practitioners often rely on broad audience definitions, despite considering “knowing their readers” essential. Evaluation primarily relies on peer feedback or social metrics rather than user testing. From these accounts, we identify recurring strategies employed to reach general, often undefined publics. We discuss implications for audience-centered authoring tools, proposing features such as persona simulations and content-adaptive multi-format authoring, message-first rhetoric-aware workflows, and lightweight in-tool evaluation to better support the realities of public-facing design.

Abstract A range of charts with different strengths and weaknesses exists to support the visual analysis of univariate distributions, with a limited understanding of which charts best support which tasks and users, and how practitioners use charts. We categorize the available charts for univariate distributions into four groups and present the results of a mixed‐methods comparison (n=215) of participants' perception and preferences across boxplots, violinplots, jittered stripplots, and histograms as representatives of their respective categories. The click‐to‐select approach in our study, combined with data on participants' subjective experiences and preferences, allows to both measure accuracy on benchmark tasks and discuss participants' choices qualitatively. Our analysis reveals differences between charts in task accuracy, common misunderstandings, and preferences across various low‐level tasks, and indicates that chart preference and familiarity do not necessarily align with participants' task performance. Interviews with five visualization practitioners further reveal that charts widely preferred by general audiences (such as histograms) or commonly used in scientific domains (such as boxplots) are not inherently the most effective for all tasks.

Contemporary discourse on data communication has discussed logos (reason) and, more recently, ethos (credibility) extensively. While the concept of pathos (emotional appeal) has received growing attention in the visualization community in recent years, its connection to related concepts such as rhetoric and aesthetics remains underexplored. In this paper, we provide working definitions of these terms, contextualize them within data visualization, and explore their overlaps and differences in light of their historical development. This historical perspective offers a more holistic understanding of how these approaches in science and philosophy have evolved over time, contributing to a deeper comprehension of their integration into the design process. Drawing on Campbell's seven circumstances, we illustrate how pathos functions as a rhetorical strategy in contemporary data visualizations, examining the interplay of rhetorical strategies, aesthetic qualities, and offering our interpretation of how these elements work together.

How audiences read, interpret, and critique data visualizations is mainly assessed through performance tests featuring tasks like value retrieval. Yet, other factors shown to shape visualization understanding, such as numeracy, graph familiarity, and aesthetic perception, remain underrepresented in existing instruments. To address this, we design and test a Multidimensional Assessment Method for Visualization Understanding (MdamV). This method integrates task-based measures with self-perceived ability ratings and open-ended critique, applied directly to the visualizations being read. Grounded in learning sciences frameworks that view understanding as a multifaceted process, MdamV spans six dimensions: Comprehending, Decoding, Aestheticizing, Critiquing, Reading, and Contextualizing. Validation was supported by a survey (N = 438) representative of Austria's population (ages 18-74, male/female split), using a line chart and a bar chart on climate data. Findings show, for example, that about a quarter of respondents indicate deficits in comprehending simple data units, roughly one in five people felt unfamiliar with each chart type, and self-assessed numeracy was significantly related to data reading performance (p = 0.0004). Overall, the evaluation of MdamV demonstrates the value of assessing visualization understanding beyond performance, framing it as a situated process tied to particular visualizations.

Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the “XAI Novice Question Bank”, an extension of the XAI Question Bank (Liao et al., 2020) containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences. • People affected by algorithmic systems should be better considered in explainable AI. • Their interests lie in a system’s context and usage rather than in technical details. • Explanations must meet their information needs in order to support their agency. • Leveraging cognitive processes could improve the understandability of explanations. • Affected people’s perceptions of risks and benefits impact their information needs.

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights.

We present Cell2Cell, a novel visual analytics approach for quantifying and visualizing networks of cell-cell interactions in three-dimensional (3D) multi-channel cancerous tissue data. By analyzing cellular interactions, biomedical experts can gain a more accurate understanding of the intricate relationships between cancer and immune cells. Recent methods have focused on inferring interaction based on the proximity of cells in low-resolution 2D multi-channel imaging data. By contrast, we analyze cell interactions by quantifying the presence and levels of specific proteins within a tissue sample (protein expressions) extracted from high-resolution 3D multi-channel volume data. Such analyses have a strong exploratory nature and require a tight integration of domain experts in the analysis loop to leverage their deep knowledge. We propose two complementary semi-automated approaches to cope with the increasing size and complexity of the data interactively: On the one hand, we interpret cell-to-cell interactions as edges in a cell graph and analyze the image signal (protein expressions) along those edges, using spatial as well as abstract visualizations. Complementary, we propose a cell-centered approach, enabling scientists to visually analyze polarized distributions of proteins in three dimensions, which also captures neighboring cells with biochemical and cell biological consequences. We evaluate our application in three case studies, where biologists and medical experts use Cell2Cell to investigate tumor micro-environments to identify and quantify T-cell activation in human tissue data. We confirmed that our tool can fully solve the use cases and enables a streamlined and detailed analysis of cell-cell interactions.

Data visualizations play a critical role in both communicating scientific evidence about climate change and in stimulating engagement and action. To investigate how visualizations can be better utilized to communicate the complexities of climate change to different audiences, we conducted interviews with 17 experts in the fields of climate change, data visualization, and science communication, as well as with 12 laypersons. Besides questions about climate change communication and various aspects of data visualizations, we also asked participants to share what they think is the main takeaway message for two exemplary climate change data visualizations. Through a thematic analysis, we observe differences regarding the included contents, the length and abstraction of messages, and the sensemaking process between and among the participant groups. On average, experts formulated shorter and more abstract messages, often referring to higher-level conclusions rather than specific details. We use our findings to reflect on design decisions for creating more effective visualizations, particularly in news media sources geared toward lay audiences. We hereby discuss the adaption of contents according to the needs of the audience, the trade-off between simplification and accuracy, as well as techniques to make a visualization attractive.

Video summaries of scientific publications have gained more and more popularity over the last years, requiring many researchers to familiarize themselves with the tools and techniques of video production which can be an overwhelming task. This paper introduces a video structuring framework embedded into the authoring tool Pub2Vid. The tool supports users with the creation of their video outline and script, providing real video examples and recommendations based on the analysis of 40 publication summarization videos which were rated in a user study with 68 participants. Following a four-tier evaluation methodology, the application’s usability is assessed and improved via amateur and expert interviews, two rounds of usability tests and two case studies. It is shown that the tool and its recommendations are particularly useful for beginners due to the simple design and intuitive components as well as suggestions based on real video examples.

In this design study, we present Uncover, an interactive tool aimed at astronomers to find previously unidentified member stars in stellar clusters. We contribute data and task abstraction in the domain of astronomy and provide an approach for the non-trivial challenge of finding a suitable hyper-parameter set for highly flexible novelty detection models. We achieve this by substituting the tedious manual trial and error process, which usually results in finding a small subset of passable models with a five-step workflow approach. We utilize ranges of a priori defined, interpretable summary statistics models have to adhere to. Our goal is to enable astronomers to use their domain expertise to quantify model goodness effectively. We attempt to change the current culture of blindly accepting a machine learning model to one where astronomers build and modify a model based on their expertise. We evaluate the tools' usability and usefulness in a series of interviews with domain experts.

Data science is the practice of deriving insight from data, enabled by modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Data science draws from methodology developed in such fields as applied mathematics, statistics, machine learning, data mining, data management, visualization, and HCI. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond. Machine learning and data mining and visualization are integral parts of data science, and essential to enable sophisticated analysis of data. Nevertheless, both research areas are currently still rather separated and investigated by different communities rather independently. The goal of this workshop is to bring researchers from both communities together in order to discuss common interests, to talk about practical issues in application-related projects, and to identify open research problems. This summary gives a brief overview of the ACM KDD Workshop on Visualization in Data Science (VDS at ACM KDD and IEEE VIS), which will take place virtually on Aug 14-18, 2022 (Held in conjunction with KDD'22). The workshop website is available at http://www.visualdatascience.org/2022/

This article introduces embComp, a novel approach for comparing two embeddings that capture the similarity between objects, such as word and document embeddings. We survey scenarios where comparing these embedding spaces is useful. From those scenarios, we derive common tasks, introduce visual analysis methods that support these tasks, and combine them into a comprehensive system. One of embComp's central features are overview visualizations that are based on metrics for measuring differences in the local structure around objects. Summarizing these local metrics over the embeddings provides global overviews of similarities and differences. Detail views allow comparison of the local structure around selected objects and relating this local information to the global views. Integrating and connecting all of these components, embComp supports a range of analysis workflows that help understand similarities and differences between embedding spaces. We assess our approach by applying it in several use cases, including understanding corpora differences via word vector embeddings, and understanding algorithmic differences in generating embeddings.

Linguists use annotated text collections to validate, refute and refine a hypothesis about the written language. This research requires the creation and analysis of complex queries which are often above the technical expertise of the domain users. In this paper, we present a tool-design which enables language researchers to easily query annotated text corpora and conduct a comparative multi-faceted analysis on a single screen. The results of the iterative design process, including requirement analysis, multiple prototyping and user evaluation sessions, and expert reviews, are documented in detail. Our tool, called CorpSum, shows a 43.12 point increase in the mean SUS score in a randomized within-subjects test and an improvement of 3.18 times in mean task completion duration compared to a conventional solution. Two detailed case studies with linguists demonstrate a significant improvement for solving the real-world problems of the domain users.

We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation. DDR renders pseudo-document pages by modeling randomized textual and non-textual contents of interest, with user-defined layout and font styles to support joint learning of fine-grained classes. We demonstrate competitive results using our DDR approach to extract nine document classes from the benchmark CS-150 and papers published in two domains, namely annual meetings of Association for Computational Linguistics (ACL) and IEEE Visualization (VIS). We compare DDR to conditions of style mismatch, fewer or more noisy samples that are more easily obtained in the real world. We show that high-fidelity semantic information is not necessary to label semantic classes but style mismatch between train and test can lower model accuracy. Using smaller training samples had a slightly detrimental effect. Finally, network models still achieved high test accuracy when correct labels are diluted towards confusing labels; this behavior hold across several classes.

Data science is the practice of deriving insight from data, enabled by modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Data science draws from methodology developed in such fields as applied mathematics, statistics, machine learning, data mining, data management, visualization, and HCI. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond. Machine learning and data mining and visualization are integral parts of data science, and essential to enable sophisticated analysis of data. Nevertheless, both research areas are currently still rather separated and investigated by different communities rather independently. The goal of this workshop is to bring researchers from both communities together in order to discuss common interests, to talk about practical issues in application-related projects, and to identify open research problems. This summary gives a brief overview of the ACM KDD Workshop on Visualization in Data Science (VDS at ACM KDD and IEEE VIS), which will take place virtually on Aug 14-18, 2021 (Held in conjunction with KDD'21). The workshop website is available at: http://www.visualdatascience.org/2021/

We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.

In this work, we introduce a novel distance metric that describes the distance between sets of events, where events in the most common form are actions that happen at a given time. More generally, an event can be any object that is in an ordered relation to other objects. In our case, an event is a course taken by a student that happens during a specific semester. Calculating the distance uses the difference between the positional relations of all individual events in the set. For this, we do not use the absolute position of events but instead use the sum of differences of the relations before, concurrent, and after to express distance. We describe our metric algorithmically and evaluate it formally as well as exemplary on an existing data set of student exams. We also show that the results of the metric are intuitive to interpret for humans by comparing them to the results of a user study that we ran.This metric can be applied to a range of problems that rely on the positional relation of events by removing the dependency of timestamps for events and replacing them with a set of ordered identifiers. We show a specific application of the metric by tackling the problem of clustering and predicting study paths from university students.

Abstract We present visual analysis methods for the evaluation of tomographic fiber reconstruction algorithms by means of analysis, visual debugging and comparison of reconstructed fibers in materials science. The methods are integrated in a tool (FIAKER) that supports the entire workflow. It enables the analysis of various fiber reconstruction algorithms, of differently parameterized fiber reconstruction algorithms and of individual steps in iterative fiber reconstruction algorithms. Insight into the performance of fiber reconstruction algorithms is obtained by a list‐based ranking interface. A 3D view offers interactive visualization techniques to gain deeper insight, e.g., into the aggregated quality of the examined fiber reconstruction algorithms and parameterizations. The tool was designed in close collaboration with researchers who work with fiber‐reinforced polymers on a daily basis and develop algorithms for tomographic reconstruction and characterization of such materials. We evaluate the tool using synthetic datasets as well as tomograms of real materials. Five case studies certify the usefulness of the tool, showing that it significantly accelerates the analysis and provides valuable insights that make it possible to improve the fiber reconstruction algorithms. The main contribution of the paper is the well‐considered combination of methods and their seamless integration into a visual tool that supports the entire workflow. Further findings result from the analysis of (dis‐)similarity measures for fibers as well as from the discussion of design decisions. It is also shown that the generality of the analytical methods allows a wider range of applications, such as the application in pore space analysis.

Abstract In this paper we present Hypersliceplorer, an algorithm for generating 2D slices of multi‐dimensional shapes defined by a simplical mesh. Often, slices are generated by using a parametric form and then constraining parameters to view the slice. In our case, we developed an algorithm to slice a simplical mesh of any number of dimensions with a two‐dimensional slice. In order to get a global appreciation of the multi‐dimensional object, we show multiple slices by sampling a number of different slicing points and projecting the slices into a single view per dimension pair. These slices are shown in an interactive viewer which can switch between a global view (all slices) and a local view (single slice). We show how this method can be used to study regular polytopes, differences between spaces of polynomials, and multi‐objective optimization surfaces.

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

We present TagRefinery, an interactive visual application aiding the cleaning and processing of open tag spaces, such as those in Last.fm or YouTube. Our pre-design analysis showed a need to support a spectrum of user expertise from novice to advanced, which resulted in two distinct interface modes. Summative evaluations of TagRefinery showed that it could effectively guide the novice users through the workflow by giving them brief but helpful explanations on why each step was required, and providing visual and statistical aids to help them in making important decisions. This is while our more expert users greatly appreciated the amount of control and granularity over the workflow that our more advanced interface mode offered. Both the underlying tag cleaning workflow and the interface were designed iteratively in a participatory design process in collaboration with research on a music recommendation interface based on Last.fm tags.

Abstract Multi‐dimensional continuous functions are commonly visualized with 2D slices or topological views. Here, we explore 1D slices as an alternative approach to show such functions. Our goal with 1D slices is to combine the benefits of topological views, that is, screen space efficiency, with those of slices, that is a close resemblance of the underlying function. We compare 1D slices to 2D slices and topological views, first, by looking at their performance with respect to common function analysis tasks. We also demonstrate 3 usage scenarios: the 2D sinc function, neural network regression, and optimization traces. Based on this evaluation, we characterize the advantages and drawbacks of each of these approaches, and show how interaction can be used to overcome some of the shortcomings.

We present the results of a comprehensive multi-pass analysis of visualization paper keywords supplied by authors for their papers published in the IEEE Visualization conference series (now called IEEE VIS) between 1990-2015. From this analysis we derived a set of visualization topics that we discuss in the context of the current taxonomy that is used to categorize papers and assign reviewers in the IEEE VIS reviewing process. We point out missing and overemphasized topics in the current taxonomy and start a discussion on the importance of establishing common visualization terminology. Our analysis of research topics in visualization can, thus, serve as a starting point to (a) help create a common vocabulary to improve communication among different visualization sub-groups, (b) facilitate the process of understanding differences and commonalities of the various research sub-fields in visualization, (c) provide an understanding of emerging new research trends, (d) facilitate the crucial step of finding the right reviewers for research submissions, and (e) it can eventually lead to a comprehensive taxonomy of visualization research. One additional tangible outcome of our work is an online query tool (http://keyvis.org/) that allows visualization researchers to easily browse the 3952 keywords used for IEEE VIS papers since 1990 to find related work or make informed keyword choices.

We have created and made available to all a dataset with information about every paper that has appeared at the IEEE Visualization (VIS) set of conferences: InfoVis, SciVis, VAST, and Vis. The information about each paper includes its title, abstract, authors, and citations to other papers in the conference series, among many other attributes. This article describes the motivation for creating the dataset, as well as our process of coalescing and cleaning the data, and a set of three visualizations we created to facilitate exploration of the data. This data is meant to be useful to the broad data visualization community to help understand the evolution of the field and as an example document collection for text data visualization research.

A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.

In this paper we present a method for predicting the rendering time to display multi-dimensional data for the analysis of computer simulations using the HyperSlice [36] method with Gaussian process model reconstruction. Our method relies on a theoretical understanding of how the data points are drawn on slices and then fits the formula to a user's machine using practical experiments. We also describe the typical characteristics of data when analyzing deterministic computer simulations as described by the statistics community. We then show the advantage of carefully considering how many data points can be drawn in real time by proposing two approaches of how this predictive formula can be used in a real-world system.

We present a case study on how a team of instructors put learner-centered principles into practice in a large undergraduate course on Human-Computer Interaction (HCI) that was run in 4 parallel groups of about 50 students. The course stands on the crossroads between software engineering, business, and research in so far as student-teams apply human-centered design techniques to develop mobile apps, test them with real end-users, read research papers and regularly reflect upon their experience. As a proof of the course-concept, selected results from formative and summative assessments are presented. The summative results show that students rated the course as one of the best of the 87 computer science courses run in the summer term of 2015 at the University of Vienna. The primary goal of this paper is to provide instructors intrigued by learner-centered approaches with ideas for their own practice. In particular, this paper is of interest to those who teach Human-Computer Interaction and to those who seek inspiration on mapping their course to the 14 learner-centered principles.

We report on the design and evaluation of TagFlip, a novel interface for active music discovery based on social tags of music. The tool, which was built for phone-sized screens, couples high user control on the recommended music with minimal interaction effort. Contrary to conventional recommenders, which only allow the specification of seed attributes and the subsequent like/dislike of songs, we put the users in the centre of the recommendation process. With a library of 100,000 songs, TagFlip describes each played song to the user through its most popular tags on Last.fm and allows the user to easily specify which of the tags should be considered for the next song, or the next stream of songs. In a lab user study where we compared it to Spotify's mobile application, TagFlip came out on top in both subjective user experience (control, transparency, and trust) and our objective measure of number of interactions per liked song. Our users found TagFlip to be an important complementary experience to that of Spotify, enabling more active and directed discovery sessions as opposed to the mostly passive experience that traditional recommenders offer.

: We present ChAsE, a cross-platform desktop application developed for interactive visualization, exploration and clustering of epigenomic data such as ChIP-seq experiments. ChAsE is designed and developed in close collaboration with several groups of biologists and bioinformaticians with a focus on usability and interactivity. Data can be analyzed through k-means clustering, specifying presence or absence of signal in epigenetic data and performing set operations between clusters. Results can be explored in an interactive heat map and profile plot interface and exported for downstream analysis or as high quality figures suitable for publications. AVAILABILITY AND IMPLEMENTATION: Software, source code (MIT License), data and video tutorials available at http://chase.cs.univie.ac.at CONTACT: : [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

Abstract We present GEMSe, an interactive tool for exploring and analyzing the parameter space of multi‐channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter space and computing the corresponding segmentations. A hierarchically clustered image tree provides an overview of variations in the resulting space of label images. Details are provided through exemplary images from the selected cluster and histograms visualizing the parameters and the derived output in the selected cluster. The correlation between parameters and derived output as well as the effect of parameter changes can be explored through interactive filtering and scatter plots. We evaluate the usefulness of GEMSe through expert reviews and case studies based on three different kinds of datasets: A synthetic dataset emulating the combination of 3D X‐ray computed tomography with data from K‐Edge spectroscopy, a three‐channel scan of a rock crystal acquired by a Talbot‐Lau grating interferometer X‐ray computed tomography device, as well as a hyperspectral image.

BACKGROUND: Several tools have been developed to enable biologists to perform initial browsing and exploration of sequencing data. However the computational tool set for further analyses often requires significant computational expertise to use and many of the biologists with the knowledge needed to interpret these data must rely on programming experts. RESULTS: We present VisRseq, a framework for analysis of sequencing datasets that provides a computationally rich and accessible framework for integrative and interactive analyses without requiring programming expertise. We achieve this aim by providing R apps, which offer a semi-auto generated and unified graphical user interface for computational packages in R and repositories such as Bioconductor. To address the interactivity limitation inherent in R libraries, our framework includes several native apps that provide exploration and brushing operations as well as an integrated genome browser. The apps can be chained together to create more powerful analysis workflows. CONCLUSIONS: To validate the usability of VisRseq for analysis of sequencing data, we present two case studies performed by our collaborators and report their workflow and insights.

The assessment of expression and epigenomic status using sequencing based methods provides an unprecedented opportunity to identify and correlate allelic differences with epigenomic status. We present ALEA, a computational toolbox for allele-specific epigenomics analysis, which incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. ALEA provides a customizable pipeline of command line tools for allele-specific analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. The pipeline has been validated using human and hybrid mouse ChIP-seq and RNA-seq data. AVAILABILITY: The package, test data and usage instructions are available online at http://www.bcgsc.ca/platform/bioinfo/software/alea CONTACT: : [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and systematize research endeavors in this area, we provide a conceptual framework for visual parameter space analysis problems. The framework is based on our own experience and a structured analysis of the visualization literature. It contains three major components: (1) a data flow model that helps to abstractly describe visual parameter space analysis problems independent of their application domain; (2) a set of four navigation strategies of how parameter space analysis can be supported by visualization tools; and (3) a characterization of six analysis tasks. Based on our framework, we analyze and classify the current body of literature, and identify three open research gaps in visual parameter space analysis. The framework and its discussion are meant to support visualization designers and researchers in characterizing parameter space analysis problems and to guide their design and evaluation processes.

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

Abstract In this design study, we present an analysis and abstraction of the data and tasks related to the domain of epigenomics, and the design and implementation of an interactive tool to facilitate data analysis and visualization in this domain. Epigenomic data can be grouped into subsets either by k‐means clustering or by querying for combinations of presence or absence of signal (on/off) in different epigenomic experiments. These steps can easily be interleaved and the comparison of different workflows is explicitly supported. We took special care to contain the exponential expansion of possible on/off combinations by creating a novel querying interface. An interactive heat map facilitates the exploration and comparison of different clusters. We validated our iterative design by working closely with two groups of biologists on different biological problems. Both groups quickly found new insight into their data as well as claimed that our tool would save them several hours or days of work over using existing tools.

We present an assessment of the state and historic development of evaluation practices as reported in papers published at the IEEE Visualization conference. Our goal is to reflect on a meta-level about evaluation in our community through a systematic understanding of the characteristics and goals of presented evaluations. For this purpose we conducted a systematic review of ten years of evaluations in the published papers using and extending a coding scheme previously established by Lam et al. [2012]. The results of our review include an overview of the most common evaluation goals in the community, how they evolved over time, and how they contrast or align to those of the IEEE Information Visualization conference. In particular, we found that evaluations specific to assessing resulting images and algorithm performance are the most prevalent (with consistently 80-90% of all papers since 1997). However, especially over the last six years there is a steady increase in evaluation methods that include participants, either by evaluating their performances and subjective feedback or by evaluating their work practices and their improved analysis and reasoning capabilities using visual tools. Up to 2010, this trend in the IEEE Visualization conference was much more pronounced than in the IEEE Information Visualization conference which only showed an increasing percentage of evaluation through user performance and experience testing. Since 2011, however, also papers in IEEE Information Visualization show such an increase of evaluations of work practices and analysis as well as reasoning using visual tools. Further, we found that generally the studies reporting requirements analyses and domain-specific work practices are too informally reported which hinders cross-comparison and lowers external validity.

In this paper, we introduce ParaGlide, a visualization system designed for interactive exploration of parameter spaces of multidimensional simulation models. To get the right parameter configuration, model developers frequently have to go back and forth between setting input parameters and qualitatively judging the outcomes of their model. Current state-of-the-art tools and practices, however, fail to provide a systematic way of exploring these parameter spaces, making informed decisions about parameter configurations a tedious and workload-intensive task. ParaGlide endeavors to overcome this shortcoming by guiding data generation using a region-based user interface for parameter sampling and then dividing the model's input parameter space into partitions that represent distinct output behavior. In particular, we found that parameter space partitioning can help model developers to better understand qualitative differences among possibly high-dimensional model outputs. Further, it provides information on parameter sensitivity and facilitates comparison of models. We developed ParaGlide in close collaboration with experts from three different domains, who all were involved in developing new models for their domain. We first analyzed current practices of six domain experts and derived a set of tasks and design requirements, then engaged in a user-centered design process, and finally conducted three longitudinal in-depth case studies underlining the usefulness of our approach.

Many data sets are sampled on regular lattices in two, three or more dimensions, and recent work has shown that statistical properties of these data sets must take into account the continuity of the underlying physical phenomena. However, the effects of quantization on the statistics have not yet been accounted for. This paper therefore reconciles the previous papers to the underlying mathematical theory, develops a mathematical model of quantized statistics of continuous functions, and proves convergence of geometric approximations to continuous statistics for regular sampling lattices. In addition, the computational cost of various approaches is considered, and recommendations made about when to use each type of statistic.

Abstract In this design study, we present an analysis and abstraction of the data and task in the domain of fisheries management, and the design and implementation of the Vismon tool to address the identified requirements. Vismon was designed to support sophisticated data analysis of simulation results by managers who are highly knowledgeable about the fisheries domain but not experts in simulation software and statistical data analysis. The previous workflow required the scientists who built the models to spearhead the analysis process. The features of Vismon include sensitivity analysis, comprehensive and global trade‐offs analysis, and a staged approach to the visualization of the uncertainty of the underlying simulation model. The tool was iteratively refined through a multi‐year engagement with fisheries scientists with a two‐phase approach, where an initial diverging experimentation phase to test many alternatives was followed by a converging phase where the set of multiple linked views that proved effective were integrated together in a useable way. Several fisheries scientists have used Vismon to communicate with policy makers, and it is scheduled for deployment to policy makers in Alaska.

We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O ( n ) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters.

In this paper, we present two methods for accurate gradient estimation from scalar field data sampled on regular lattices. The first method is based on the multidimensional Taylor series expansion of the convolution sum and allows us to specify design criteria such as compactness and approximation power. The second method is based on a Hilbert space framework and provides a minimum error solution in the form of an orthogonal projection operating between two approximation spaces. Both methods lead to discrete filters, which can be combined with continuous reconstruction kernels to yield highly accurate estimators as compared to the current state of the art. We demonstrate the advantages of our methods in the context of volume rendering of data sampled on Cartesian and Body-Centered Cubic lattices. Our results show significant qualitative and quantitative improvements for both synthetic and real data, while incurring a moderate preprocessing and storage overhead.

The Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices have been analytically shown to be more efficient sampling lattices than the traditional Cartesian Cubic (CC) lattice, but there has been no estimate of their visual comparability. Two perceptual studies (each with N = 12 participants) compared the visual quality of images rendered from BCC and FCC lattices to images rendered from the CC lattice. Images were generated from two signals: the commonly used Marschner-Lobb synthetic function and a computed tomography scan of a fish tail. Observers found that BCC and FCC could produce images of comparable visual quality to CC, using 30-35 percent fewer samples. For the images used in our studies, the L(2) error metric shows high correlation with the judgement of human observers. Using the L(2) metric as a proxy, the results of the experiments appear to extend across a wide range of images and parameter choices.

Sort-first distributions have been studied and used far less than sort-last distributions for parallel volume rendering, especially when the data are too large to be replicated fully. We demonstrate that sort-first distributions are not only a viable method of performing data-scalable parallel volume rendering, but more importantly they allow for a range of rendering algorithms and techniques that are not efficient with sort-last distributions. Several of these algorithms are discussed and two of them are implemented in a parallel environment: a new improved variant of early ray termination to speed up rendering when volumetric occlusion occurs and a volumetric shadowing technique that produces more realistic and informative images based on half angle slicing. Improved methods of distributing the computation of the load balancing and loading portions of a subdivided data set are also presented. Our detailed test results for a typical GPU cluster with distributed memory show that our sort-first rendering algorithm outperforms sort-last rendering in many scenarios.

In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.

Abstract In this paper, we introduce the concept of isosurface similarity maps for the visualization of volume data. Iso‐surface similarity maps present structural information of a volume data set by depicting similarities between individual isosurfaces quantified by a robust information‐theoretic measure. Unlike conventional histograms, they are not based on the frequency of isovalues and/or derivatives and therefore provide complementary information. We demonstrate that this new representation can be used to guide transfer function design and visualization parameter specification. Furthermore, we use isosurface similarity to develop an automatic parameter‐free method for identifying representative isovalues. Using real‐world data sets, we show that isosurface similarity maps can be a useful addition to conventional classification techniques.

Abstract In this paper we study the comprehensive effects on volume rendered images due to numerical errors caused by the use of finite precision for data representation and processing. To estimateactualerror behavior we conduct a thorough study using a volume renderer implemented with arbitrary floating‐point precision. Based on the experimental data we then model the impact of floating‐point pipeline precision, sampling frequency and fixed‐point input data quantization on the fidelity of rendered images. We introduce three models, an average model, which does not adapt to different data nor varying transfer functions, as well as two adaptive models that take the intricacies of a new data set and transfer function into account by adapting themselves given a few different images rendered. We also test and validate our models based on new data that was not used during our model building.

Abstract In this paper, we develop an interactive analysis and visualization tool for probabilistic segmentation results in medical imaging. We provide a systematic approach to analyze, interact and highlight regions of segmentation uncertainty. We introduce a set of visual analysis widgets integrating different approaches to analyze multivariate probabilistic field data with direct volume rendering. We demonstrate the user's ability to identify suspicious regions (e.g. tumors) and correct the misclassification results using a novel uncertainty‐based segmentation editing technique. We evaluate our system and demonstrate its usefulness in the context of static and time‐varying medical imaging datasets.

We investigate the use of a Fourier-domain derivative error kernel to quantify the error incurred while estimating the gradient of a function from scalar point samples on a regular lattice. We use the error kernel to show that gradient reconstruction quality is significantly enhanced merely by shifting the reconstruction kernel to the centers of the principal lattice directions. Additionally, we exploit the algebraic similarities between the scalar and derivative error kernels to design asymptotically optimal gradient estimation filters that can be factored into an infinite impulse response interpolation prefilter and a finite impulse response directional derivative filter. This leads to a significant performance gain both in terms of accuracy and computational efficiency. The interpolation prefilter provides an accurate scalar approximation and can be re-used to cheaply compute directional derivatives on-the-fly without the need to store gradients. We demonstrate the impact of our filters in the context of volume rendering of scalar data sampled on the Cartesian and Body-Centered Cubic lattices. Our results rival those obtained from other competitive gradient estimation methods while incurring no additional computational or storage overhead.

Graphics artists commonly employ physically-based simulation for the generation of effects such as smoke, explosions, and similar phenomena. The task of finding the correct parameters for a desired result, however, is difficult and time-consuming as current tools provide little to no guidance. In this paper, we present a new approach for the visual exploration of such parameter spaces. Given a three-dimensional scene description, we utilize sampling and spatio-temporal clustering techniques to generate a concise overview of the achievable variations and their temporal evolution. Our visualization system then allows the user to explore the simulation space in a goal-oriented manner. Animation sequences with a set of desired characteristics can be composed using a novel search-by-example approach and interactive direct volume rendering is employed to provide instant visual feedback. A user study was performed to evaluate the applicability of our system in production use.

These are the proceedings of the IEEE Visualization Conference 2010 (Vis 2010) and the IEEE Information Visualization Conference 2010 (InfoVis 2010) held during October 24 to 29, 2010 in Salt Lake City, Utah, USA.

In this paper we present a novel anisotropic diffusion model targeted for 3D scalar field data. Our model preserves material boundaries as well as fine tubular structures while noise is smoothed out. One of the major novelties is the use of the directional second derivative to define material boundaries instead of the gradient magnitude for thresholding. This results in a diffusion model that has much lower sensitivity to the diffusion parameter and smoothes material boundaries consistently compared to gradient magnitude based techniques. We empirically analyze the stability and convergence of the proposed diffusion and demonstrate its de-noising capabilities for both analytic and real data. We also discuss applications in the context of volume rendering.

We develop an interactive analysis and visualization tool for probabilistic segmentation in medical imaging. The originality of our approach is that the data exploration is guided by shape and appearance knowledge learned from expert-segmented images of a training population. We introduce a set of multidimensional transfer function widgets to analyze the multivariate probabilistic field data. These widgets furnish the user with contextual information about conformance or deviation from the population statistics. We demonstrate the user's ability to identify suspicious regions (e.g. tumors) and to correct the misclassification results. We evaluate our system and demonstrate its usefulness in the context of static anatomical and time-varying functional imaging datasets.

We undertake a study of the local properties of 2-Gabriel meshes: manifold triangle meshes each of whose faces has an open Euclidean diametric ball that contains no mesh vertices. We show that, under mild constraints on the dihedral angles, such meshes are Delaunay meshes: the open geodesic circumdisk of each face contains no mesh vertex.

Abstract We present a design technique for colors with the purpose of lowering the energy consumption of the display device. Our approach is based on a screen space variant energy model. The result of our design is a set of distinguishable iso‐lightness colors guided by perceptual principles. We present two variations of our approach. One is based on a set of discrete user‐named (categorical) colors, which are analyzed according to their energy consumption. The second is based on the constrained continuous optimization of color energy in the perceptually uniform CIELAB color space. We quantitatively compare our two approaches with a traditional choice of colors, demonstrating that we typically save approximately 40 percent of the energy. The color sets are applied to examples from the 2D visualization of nominal data and volume rendering of 3D scalar fields.

Abstract Within the context of emission tomography, we study volumetric reconstruction methods based on the Expectation Maximization (EM) algorithm. We show, for the first time, the equivalence of the standard implementation of the EM‐based reconstruction with an implementation based on hardware‐accelerated volume rendering for nearest‐neighbor (NN) interpolation. This equivalence suggests that higher‐order kernels should be used with caution and do not necessarily lead to better performance. We also show that the EM algorithm can easily be adapted for different lattices, the body‐centered cubic (BCC) one in particular. For validation purposes, we use the 3D version of the Shepp‐Logan synthetic phantom, for which we derive closed‐form analytical expressions of the projection data. The experimental results show the theoretically‐predicted optimality of NN interpolation in combination with the EM algorithm, for both the noiseless and the noisy case. Moreover, reconstruction on the BCC lattice leads to superior accuracy, more compact data representation, and better noise reduction compared to the Cartesian one. Finally, we show the usefulness of the proposed method for optical projection tomography of a mouse embryo.

Abstract In this paper we present a novel framework for the visualization and reconstruction from non‐uniform point sets. We adopt a variational method for the reconstruction of 3D non‐uniform data to a uniform grid of chosen resolution. We will extend this reconstruction to an efficient multi‐resolution uniform representation of the underlying data. Our multi‐resolution representation includes a traditional bottom‐up approach and a novel top‐down hierarchy for adaptive hierarchical reconstruction. Using a hybrid regularization functional we can improve the reconstruction results. Finally, we discuss further application scenarios and show rendering results to emphasize the effectiveness and quality of our proposed framework. By means of qualitative results and error comparisons we demonstrate superiority of our method compared to competing methods.

Full spectra allow the generation of a physically correct rendering of a scene under different lighting conditions. In this article we devise a tool to augment a palette of given lights and material reflectances with constructed spectra, yielding specified colors or spectral properties such as metamerism or objective color constancy. We utilize this to emphasize or hide parts of a scene by matching or differentiating colors under different illuminations. These color criteria are expressed as a quadratic programming problem, which may be solved with positivity constraints. Further, we characterize full spectra of lights, surfaces, and transmissive materials in an efficient linear subspace model by forming eigenvectors of sets of spectra and transform them to an intermediate space in which spectral interactions reduce to simple component-wise multiplications during rendering. The proposed method enhances the user's freedom in designing photo-realistic scenes and helps in creating expressive visualizations. A key application of our technique is to use specific spectral lighting to scale the visual complexity of a scene by controlling visibility of texture details in surface graphics or material details in volume rendering.

In this paper, we extend the single relaxation time Lattice-Boltzmann Method (LBM) to the 3D body-centered cubic (BCC) lattice. We show that the D3bQ15 lattice defined by a 15 neighborhood connectivity of the BCC lattice is not only capable of more accurately discretizing the velocity space of the continuous Boltzmann equation as compared to the D3Q15 Cartesian lattice, it also achieves a comparable spatial discretization with 30 percent less samples. We validate the accuracy of our proposed lattice by investigating its performance on the 3D lid-driven cavity flow problem and show that the D3bQ15 lattice offers significant cost savings while maintaining a comparable accuracy. We demonstrate the efficiency of our method and the impact on graphics and visualization techniques via the application of line-integral convolution on 2D slices as well as the extraction of streamlines of the 3D flow. We further study the benefits of our proposed lattice by applying it to the problem of simulating smoke and show that the D3bQ15 lattice yields more detail and turbulence at a reduced computational cost.

We propose a new perception-guided compositing operator for color blending. The operator maintains the same rules for achromatic compositing as standard operators (such as the over operator), but it modifies the computation of the chromatic channels. Chromatic compositing aims at preserving the hue of the input colors; color continuity is achieved by reducing the saturation of colors that are to change their hue value. The main benefit of hue preservation is that color can be used for proper visual labeling, even under the constraint of transparency rendering or image overlays. Therefore, the visualization of nominal data is improved. Hue-preserving blending can be used in any existing compositing algorithm, and it is particularly useful for volume rendering. The usefulness of hue-preserving blending and its visual characteristics are shown for several examples of volume visualization.

Abstract We develop adaptive sampling criteria which guarantee a topologically faithful mesh and demonstrate an improvement and simplification over earlier results, albeit restricted to 2D surfaces. These sampling criteria are based on functions defined by intrinsic properties of the surface: the strong convexity radius and the injectivity radius. We establish inequalities that relate these functions to the local feature size, thus enabling a comparison between the demands of the intrinsic sampling criteria and those based on Euclidean distances and the medial axis.

We introduce a family of box splines for efficient, accurate and smooth reconstruction of volumetric data sampled on the Body Centered Cubic (BCC) lattice, which is the favorable volumetric sampling pattern due to its optimal spectral sphere packing property. First, we construct a box spline based on the four principal directions of the BCC lattice that allows for a linear C(0) reconstruction. Then, the design is extended for higher degrees of continuity. We derive the explicit piecewise polynomial representation of the C(0) and C(2) box splines that are useful for practical reconstruction applications. We further demonstrate that approximation in the shift-invariant space---generated by BCC-lattice shifts of these box splines---is {twice} as efficient as using the tensor-product B-spline solutions on the Cartesian lattice (with comparable smoothness and approximation order, and with the same sampling density). Practical evidence is provided demonstrating that not only the BCC lattice is generally a more accurate sampling pattern, but also allows for extremely efficient reconstructions that outperform tensor-product Cartesian reconstructions.

The three papers in this special section are extended versions of three papers from the Ninth Eurographics/IEEE VGTC Symposium on Visualization (EuroVis '07), held in Norrkoping, Sweden, May 23-25, 2007. The papers are summarized here.

We use a haptically enhanced mixing board with a video projector as an interface to various data visualization tasks. We report results of an expert review with four participants, qualitatively evaluating task), parallel coordinates interface (multi-dimensional combinato-rial search), and ExoVis (3D spatial navigation). Our investigation sought to determine the strengths of this physical input given its ca-pability to facilitate bimanual interaction, constraint maintenance, tight coupling of input and output, and other features. Participants generally had little difficulty with the mappings of parameters to sliders. The graspable sliders apparently reduced the mental exer-tion needed to acquire control, allowing participants to attend more directly to understanding the visualization. Participants often des-ignated specific roles for each hand, but only rarely moved both hands simultaneously.

In this work, we propose and compare several methods for the visualization and exploration of time-varying volumetric medical images based on the temporal characteristics of the data. The principle idea is to consider a time-varying data set as a 3D array where each voxel contains a time-activity curve (TAC). We define and appraise three different TAC similarity measures. Based on these measures we introduce three methods to analyze and visualize time-varying data. The first method relates the whole data set to one template TAC and creates a 1D histogram. The second method extends the 1D histogram into a 2D histogram by taking the Euclidean distance between voxels into account. The third method does not rely on a template TAC but rather creates a 2D scatter-plot of all TAC data points via multi-dimensional scaling. These methods allow the user to specify transfer functions on the 1D and 2D histograms and on the scatter plot, respectively. We validate these methods on synthetic dynamic SPECT and PET data sets and a dynamic planar Gamma camera image of a patient. These techniques are designed to offer researchers and health care professionals a new tool to study the time-varying medical imaging data sets.

We present a novel rendering algorithm that analyses the ray profiles along the line of sight. The profiles are subdivided according to encountered peaks and valleys at so called transition points. The sensitivity of these transition points is calibrated via two thresholds. The slope threshold is based on the magnitude of a peak following a valley, while the peeling threshold measures the depth of the transition point relative to the neighboring rays. This technique separates the dataset into a number of feature layers. The user can scroll through the layers inspecting various features from the current view position. While our technique has been inspired by opacity peeling approach, we demonstrate that we can reveal detectable features even in the third and forth layers for both CT and MRI datasets.

This paper presents a user study of the visual quality of an imaging pipeline employing the optimal body-centered cubic (BCC) sampling lattice. We provide perceptual evidence supporting the theoretical expectation that sampling and reconstruction on the BCC lattice offer superior imaging quality over the traditionally popular Cartesian cubic (CC) sampling lattice. We asked 12 participants to choose the better of two images: one image rendered from data sampled on the CC lattice and one image that is rendered from data sampled on the BCC lattice. We used both synthetic and CT volumetric data, and confirm that the theoretical advantages of BCC sampling carry over to the perceived quality of rendered images. Using 25% to 35% fewer samples, BCC sampled data result in images that exhibit comparable visual quality to their CC counterparts.

In this paper, we are concerned with Delaunay triangulations of the vertex set of a piecewise flat (pwf) surface. We first propose the notion of well-formed Voronoi diagrams and establish a precise dual relationship between them and proper Delaunay triangulations on pwf surfaces. Then we provide an algorithm which, given any input manifold triangle mesh, constructs a Delaunay mesh: a manifold triangle mesh whose edges form an intrinsic Delaunay triangulation of its vertex set. Rather than relying on a geodesic Delaunay triangulation on the input mesh, our algorithm swaps the physical mesh edges based on the locally Delaunay criterion. We prove that when a physical edge that is not locally Delaunay is swapped, the surface area of the mesh is reduced. In order to ensure a proper Delaunay triangulation, some new vertices may need to be introduced, leading to a refinement scheme, and we detail the cases involved.

We propose a three dimensional multi-resolution scheme to represent volumetric data in resolutions which are powers of two, resolving the rigidity of the commonly used separable Cartesian multi-resolution schemes in 3D that only allow for change of resolution by a power of eight. Through in-depth comparisons with the counterpart resampling solutions on the Cartesian lattice, we demonstrate the superiority of our subsampling scheme. We derive and document the Fourier domain analysis of this representation. Using such an analysis one can obtain ideal and discrete multidimensional filters for this multi-resolution scheme.

We propose a novel method to improve the quality of multi-resolution visualizations. We reduce aliasing artifacts by approximating the data distribution with a Gaussian basis function at each level of detail for more accurate rendering at coarser levels of detail. We then show an efficient implementation of our novel Gaussian based approximation scheme and show its superiority using numerical tests and compelling renderings.

In this paper we investigate the effects of function composition in the form g(f(x)) = h(x) by means of a spectral analysis of h. We decompose the spectral description of h(x) into a scalar product of the spectral description of g(x) and a term that solely depends on f(x) and that is independent of g(x). We then use the method of stationary phase to derive the essential maximum frequency of g(f(x)) bounding the main portion of the energy of its spectrum. This limit is the product of the maximum frequency of g(x) and the maximum derivative of f(x). This leads to a proper sampling of the composition h of the two functions g and f. We apply our theoretical results to a fundamental open problem in volume rendering-the proper sampling of the rendering integral after the application of a transfer function. In particular, we demonstrate how the sampling criterion can be incorporated in adaptive ray integration, visualization with multi-dimensional transfer functions, and pre-integrated volume rendering.

We review schemes for dividing cubic cells into simplices (tetrahedra) for interpolating from sampled data to IR3, present visual and geometric artifacts generated in isosurfaces and volume renderings, and discuss how these artifacts relate to the filter kernels corresponding to the subdivision schemes.

In this article we propose a box spline and its variants for reconstructing volumetric data sampled on the Cartesian lattice. In particular we present a tri-variate box spline reconstruction kernel that is superior to tensor product reconstruction schemes in terms of recovering the proper Cartesian spectrum of the underlying function. This box spline produces a C2 reconstruction that can be considered as a three dimensional extension of the well known Zwart-Powell element in 2D. While its smoothness and approximation power are equivalent to those of the tri-cubic B-spline, we illustrate the superiority of this reconstruction on functions sampled on the Cartesian lattice and contrast it to tensor product B-splines. Our construction is validated through a Fourier domain analysis of the reconstruction behavior of this box spline. Moreover, we present a stable method for evaluation of this box spline by means of a decomposition. Through a convolution, this decomposition reduces the problem to evaluation of a four directional box spline that we previously published in its explicit closed form.

We describe a series of experiments that compare 2D displays, 3D displays, and combined 2D/3D displays (orientation icon, ExoVis, and clip planes) for relative position estimation, orientation, and volume of interest tasks. Our results indicate that 3D displays can be very effective for approximate navigation and relative positioning when appropriate cues, such as shadows, are present. However, 3D displays are not effective for precise navigation and positioning except possibly in specific circumstances, for instance, when good viewing angles or measurement tools are available. For precise tasks in other situations, orientation icon and ExoVis displays were better than strict 2D or 3D displays (displays consisting exclusively of 2D or 3D views). The combined displays had as good or better performance, inspired higher confidence, and allowed natural, integrated navigation. Clip plane displays were not effective for 3D orientation because users could not easily view more than one 2D slice at a time and had to frequently change the visibility of individual slices. Major factors contributing to display preference and usability were task characteristics, orientation cues, occlusion, and spatial proximity of views that were used together.

This work introduces a volume rendering technique that is conceptually based on the shear-warp factorization. We propose to perform the shear transformation in the frequency domain. Unlike the standard shear-warp algorithm, we allow for arbitrary sampling distances along the viewing rays, independent from the view direction. The accurate scaling of the volume slices is achieved by using the zero padding interpolation property. Finally, a high quality gradient estimation scheme is presented which uses the derivative theorem of the Fourier transform. Experimental results show that the presented method outperforms established algorithms in the quality of the produced images. If the data is sampled above the Nyquist rate the presented method is capable of a perfect reconstruction of the original function.

To make a spectral representation of color practicable for volume rendering, a new low-dimensional subspace method is used to act as the carrier of spectral information. With that model, spectral light material interaction can be integrated into existing volume rendering methods at almost no penalty. In addition, slow rendering methods can profit from the new technique of postillumination-generating spectral images in real-time for arbitrary light spectra under a fixed viewpoint. Thus, the capability of spectral rendering to create distinct impressions of a scene under different lighting conditions is established as a method of real-time interaction. Although we use an achromatic opacity in our rendering, we show how spectral rendering permits different data set features to be emphasized or hidden as long as they have not been entirely obscured. The use of postillumination is an order of magnitude faster than changing the transfer function and repeating the projection step. To put the user in control of the spectral visualization, we devise a new widget, a "light-dial," for interactively changing the illumination and include a usability study of this new light space exploration tool. Applied to spectral transfer functions, different lights bring out or hide specific qualities of the data. In conjunction with postillumination, this provides a new means for preparing data for visualization and forms a new degree of freedom for guided exploration of volumetric data sets.

We present a user interface, based on parallel coordinates, that facilitates exploration of volume data. By explicitly representing the visualization parameter space, the interface provides an overview of rendering options and enables users to easily explore different parameters. Rendered images are stored in an integrated history bar that facilitates backtracking to previous visualization options. Initial usability testing showed clear agreement between users and experts of various backgrounds (usability, graphic design, volume visualization, and medical physics) that the proposed user interface is a valuable data exploration tool.

We compare 2D/3D combination displays to displays with 2D and 3D views alone. Combination displays we consider are: orientation icon (i.e., side-by-side), in-place methods (e.g., clip planes), and a new method called ExoVis. We specifically analyze performance differences (i.e., time and accuracy) for 3D orientation and relative position tasks. Empirical results show that 3D displays are effective for approximate navigation and relative positioning whereas 2D/3D combination displays (orientation icon and ExoVis) are useful for precise orientation and position tasks. Combination 2D/3D displays had as good or better performance as 2D displays. Clip planes were not effective for a 3D orientation task, but may be useful when only one slice is needed.

We derive piecewise linear and piecewise cubic box spline reconstruction filters for data sampled on the body centered cubic (BCC) lattice. We analytically derive a time domain representation of these reconstruction filters and using the Fourier slice-projection theorem we derive their frequency responses. The quality of these filters, when used in reconstructing BCC sampled volumetric data, is discussed and is demonstrated with a raycaster. Moreover, to demonstrate the superiority of the BCC sampling, the resulting reconstructions are compared with those produced from similar filters applied to data sampled on the Cartesian lattice.

This work derives a family of dilation matrices for the body-centered cubic (BCC) lattice, which is optimal in the sense of spectral sphere packing. While satisfying the necessary conditions for dilation, these matrices are all cube roots of an integer scalar matrix. This property offers theoretical advantages for construction of wavelet functions in addition to the practical advantages when iterating through a perfect reconstruction filter bank based on BCC downsampling. Lastly, we factor the BCC matrix into two matrices that allow us to cascade two two-channel perfect reconstruction filter banks in order to construct a four-channel perfect reconstruction filter bank based on BCC downsampling.

Visualization can provide valuable assistance for data analysis and decision making tasks. However, how people perceive and interact with a visualization tool can strongly influence their understanding of the data as well as the system's usefulness. Human factors therefore contribute significantly to the visualization process and should play an important role in the design and evaluation of visualization tools. Several research initiatives have begun to explore human factors in visualization, particularly in perception-based design. Nonetheless, visualization work involving human factors is in its infancy, and many potentially promising areas have yet to be explored. Therefore, this paper aims to 1) review known methodology for doing human factors research, with specific emphasis on visualization, 2) review current human factors research in visualization to provide a basis for future investigation, and 3) identify promising areas for future research.

In this paper, we introduce algorithms to voxelize polygonal meshes in common sampling lattices. In the case of Cartesian lattices, we complete the separability and minimality proof for the voxelization method presented by Huang et al. We extend the ideas to general 2D lattices, including hexagonal lattices, and 3D body-centred cubic lattices. The notion of connectedness in the two lattice structures is discussed along with a novel voxelization algorithm for such lattices. Finally, we present the proof that meshes voxelized with our proposed algorithm satisfy the separability and minimality criteria.

Direct volume rendering of scalar fields uses a transfer function to map locally measured data properties to opacities and colors. The domain of the transfer function is typically the one-dimensional space of scalar data values. This paper advances the use of curvature information in multi-dimensional transfer functions, with a methodology for computing high-quality curvature measurements. The proposed methodology combines an implicit formulation of curvature with convolution-based reconstruction of the field. We give concrete guidelines for implementing the methodology, and illustrate the importance of choosing accurate filters for computing derivatives with convolution. Curvature-based transfer functions are shown to extend the expressivity and utility of volume rendering through contributions in three different application areas: nonphotorealistic volume rendering, surface smoothing via anisotropic diffusion, and visualization of isosurface uncertainty.

We present a new algorithm for solving the block matching problem which is independent of image content and is faster than other full-search methods. The method employs a novel data structure called the windowed-sum-squared-table, and uses the fast Fourier transform (FFT) in its computation of the sum squared difference (SSD) metric. Use of the SSD metric allows for higher peak signal to noise ratios than other fast block matching algorithms which require the sum of absolute difference (SAD) metric. However, because of the complex floating point and integer math used in our computation of the SSD metric, our method is aimed at software implementations only. Test results show that our method has a running time 13%-29% of that for the exhaustive search, depending on the size of the search range.

We describe a method for volume rendering using a spectral representation of colour instead of the traditional RGB model. It is shown how to use this framework for a novel exploration of datasets through enhanced transfer function design. Furthermore, our framework is extended to allow real-time re-lighting of the scene created with any rendering method. The technique of post-illumination is introduced to generate new spectral images for arbitrary light colours in real-time. Also a tool is described to design a palette of lights and materials having certain properties such as selective metamerism or colour constancy. Applied to spectral transfer functions, different light colours can accentuate or hide specific qualities of the data. In connection with post-illumination this provides a new degree of freedom for guided exploration of volumetric data, which cannot be achieved using the RGB model.

We review several schemes for dividing cubical cells into simplices (tetrahedra) in 3-D for interpolating from sampled data to R/sup 3/ or for computing isosurfaces by barycentric interpolation. We present test data that reveal the geometric artifacts that these subdivision schemes generate, and discuss how these artifacts relate to the filter kernels that correspond to the subdivision schemes.

The classification of volumetric data sets as well as their rendering algorithms are typically based on the representation of the underlying grid. Grid structures based on a Cartesian lattice are the de-facto standard for regular representations of volumetric data. In this paper we introduce a more general concept of regular grids for the representation of volumetric data. We demonstrate that a specific type of regular lattice-the so-called body-centered cubic-is able to represent the same data set as a Cartesian grid to the same accuracy but with 29.3% fewer samples. This speeds up traditional volume rendering algorithms by the same ratio, which we demonstrate by adopting a splatting implementation for these new lattices. We investigate different filtering methods required for computing the normals on this lattice. The lattice representation results also in lossless compression ratios that are better than previously reported. Although other regular grid structures achieve the same sample efficiency, the body-centered cubic is particularly easy to use. The only assumption necessary is that the underlying volume is isotropic and band-limited-an assumption that is valid for most practical data sets.

We present the problem of visualizing time-varying medical data. Two medical imaging modalities are compared-MRI and dynamic SPECT. For each modality, we examine several derived scalar and vector quantities such as the change in intensity over time, the spatial gradient, and the change of the gradient over time. We compare several methods for presenting the data, including isosurfaces, direct volume rendering, and vector visualization using glyphs. These techniques may provide more information and context than methods currently used in practice; thus it is easier to discover temporal changes and abnormalities in a data set.

Splatting is a volume rendering algorithm that combines efficient volume projection with a sparse data representation. Only voxels that have values inside the iso-range need to be considered, and these voxels can be projected via efficient rasterization schemes. In splatting, each projected voxel is represented as a radially symmetric interpolation kernel, equivalent to a fuzzy ball. Projecting such a basis function leaves a fuzzy impression, called a footprint or splat, on the screen. Splatting traditionally classifies and shades the voxels prior to projection, and thus each voxel footprint is weighted by the assigned voxel color and opacity. Projecting these fuzzy color balls provides a uniform screen image for homogeneous object regions, but leads to a blurry appearance of object edges. The latter is clearly undesirable, especially when the view is zoomed on the object. In this work, we manipulate the rendering pipeline of splatting by performing the classification and shading process after the voxels have been projected onto the screen. In this way volume contributions outside the iso-range never affect the image. Since shading requires gradients, we not only splat the density volume, using regular splats, but we also project the gradient volume, using gradient splats. However alternative to gradient splats, we can also compute the gradients on the projection plane using central differencing. This latter scheme cuts the number of footprint rasterization by a factor of four since only the voxel densities have to be projected.

We introduce a new approach for mapping texture on volumetric iso-surfaces and parametric surfaces. Our approach maps 2D images on surfaces while maintaining continuity and preserving the size of the mapped images on the models. Our approach is fully automatic. It eliminates the need for manual mapping of texture maps. We use the curvature of a surface at a point in order to continuously vary the scale of the mapped image. This makes our approach dependent only on local attributes of a point (position, normal and its derivatives) and independent of the global shape and topology of an object. Our method can map high resolution images on low resolution volumes, hence enhancing the visual appearance of rendered volume data. We describe a general framework useful for all surface types that have a C/sup 1/ continuous normal. We demonstrate the new method for painting volume data and for mapping cavities on volume data.

The paper describes three new results for volume rendering algorithms utilizing splatting. First, an antialiasing extension to the basic splatting algorithm is introduced that mitigates the spatial aliasing for high resolution volumes. Aliasing can be severe for high resolution volumes or volumes where a high depth of field leads to converging samples along the perspective axis. Next, an analysis of the common approximation errors in the splatting process for perspective viewing is presented. In this context, we give different implementations, distinguished by efficiency and accuracy, for adding the splat contributions to the image plane. We then present new results in controlling the splatting errors and also show their behavior in the framework of our new antialiasing technique. Finally, current work in progress on extensions to splatting for temporal antialiasing is demonstrated. We present a simple but highly effective scheme for adding motion blur to fast moving volumes.

The task of reconstructing the derivative of a discrete function is essential for its shading and rendering as well as being widely used in image processing and analysis. We survey the possible methods for normal estimation in volume rendering and divide them into two classes based on the delivered numerical accuracy. The three members of the first class determine the normal in two steps by employing both interpolation and derivative filters. Among these is a new method which has never been realized. The members of the first class are all equally accurate. The second class has only one member and employs a continuous derivative filter obtained through the analytic derivation of an interpolation filter. We use the new method to analytically compare the accuracy of the first class with that of the second. As a result of our analysis we show that even inexpensive schemes can in fact be more accurate than high order methods. We describe the theoretical computational cost of applying the schemes in a volume rendering application and provide guidelines for helping one choose a scheme for estimating derivatives. In particular we find that the new method can be very inexpensive and can compete with the normal estimations which pre-shade and pre-classify the volume (M. Levoy, 1988).

Splatting is a popular direct volume rendering algorithm. However, the algorithm does not correctly render cases where the volume sampling rate is higher than the image sampling rate (e.g. more than one voxel maps into a pixel). This situation arises with orthographic projections of high-resolution volumes, as well as with perspective projections of volumes of any resolution. The result is potentially severe spatial and temporal aliasing artifacts. Some volume ray-casting algorithms avoid these artifacts by employing reconstruction kernels which vary in width as the rays diverge. Unlike ray-casting algorithms, existing splatting algorithms do not have an equivalent mechanism for avoiding these artifacts. The authors propose such a mechanism, which delivers high-quality splatted images and has the potential for a very efficient hardware implementation.

We describe a new method for analyzing, classifying, and evaluating filters that can be applied to interpolation filters as well as to arbitrary derivative filters of any order. Our analysis is based on the Taylor series expansion of the convolution sum. Our analysis shows the need and derives the method for the normalization of derivative filter weights. Under certain minimal restrictions of the underlying function, we are able to compute tight absolute error bounds of the reconstruction process. We demonstrate the utilization of our methods to the analysis of the class of cubic BC-spline filters. As our technique is not restricted to interpolation filters, we are able to show that the Catmull-Rom spline filter and its derivative are the most accurate reconstruction and derivative filters, respectively, among the class of BC-spline filters. We also present a new derivative filter which features better spatial accuracy than any derivative BC-spline filter, and is optimal within our framework. We conclude by demonstrating the use of these optimal filters for accurate interpolation and gradient estimation in volume rendering.