Abstract Data visualization is an application‐driven field, that is always trying to satisfy its customers and to adapt to the demands, cultures, and workflows of many application areas. Therefore, it is difficult to keep focus on techniques and approaches that are not too application specific. A lot of good work on data visualization consists of single‐problem solutions, that cannot be easily merged into general‐purpose systems. In this talk, I will briefly review some current trends and issues, and identify some approaches that are common to many applications. One such approach in data visualization that has attracted interest from the early days is the detection of salient features, or patterns of interest in a data set. The main idea is to extract information at a higher level of abstraction from a mass of data, that is richer in semantics but much smaller in size, and that can help to define scenes and objects for visualization. This idea was pioneered in areas such as flow visualization, but is now more widely applied. It is often considered to be necessity to keep up with the ever rapidly increasing size of data sets, and the demand for interactivity in data visualization and analysis. Another generic approach in data visualization is called interactive visual analysis (TVA), consisting of a strongly interactive multiple‐linked‐view interfaces with integrated, powerful data analysis techniques taken from statistical analysis, pattern recognition, machine learning, and other fields. This is built on the assumptions that a single 2D or 3D visualization is often not enough, and spatial views can be augmented with abstract, derived data spaces; that strong interaction helps to promote insight; and that a better balance is needed between human visual inspection and computer‐based analysis and reasoning. Interestingly, an IVA interface can serve not only as an environment for exploration of low‐level data, but also for defining the high‐level features to be extracted, that should summarize the essence of the data. The high‐level features are usually highly application specific, and can only be found using theories from the application domains. The big challenge is to create environments for general purpose visual data analysis, and yet allow users to introduce advanced theories and methods from many application domains. The trend towards more integration in data visualization will be illustrated with cross‐links between very different areas, such as medical and flow visualization, and the combined use of techniques from scientific visualization and information visualization, and the absorption of other data analysis techniques. Also, historic and contemporary examples of feature extraction and interactive visual analysis will be shown.

Abstract Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model models the distribution in a high dimensional shape space, where each shape is represented by a single point. We present a design study on the intuitive exploration and visualization of shape spaces and shape models. Our approach focuses on the dual‐space nature of these spaces. The high‐dimensional shape space represents the population, whereas object space represents the shape of the 3D object associated with a point in shape space. A 3D object view provides local details for a single shape. The high dimensional points in shape space are visualized using a 2D scatter plot projection, the axes of which can be manipulated interactively. This results in a dynamic scatter plot, with the further extension that each point is visualized as a small version of the object shape that it represents. We further enhance the population‐object duality with a new type of view aimed at shape comparison. This new “shape evolution view” visualizes shape variability along a single trajectory in shape space, and serves as a link between the two spaces described above. Our three‐view exploration concept strongly emphasizes linked interaction between all spaces. Moving the cursor over the scatter plot or evolution views, shapes are dynamically interpolated and shown in the object view. Conversely, camera manipulation in the object view affects the object visualizations in the other views. We present a GPU‐accelerated implementation, and show the effectiveness of the three‐view approach using a number of real‐world cases. In these, we demonstrate how this multi‐view approach can be used to visually explore important aspects of a statistical shape model, including specificity, compactness and reconstruction error.

Abstract Kinematics is the analysis of motions without regarding forces or inertial effects, with the purpose of understanding joint behaviour. Kinematic data of linked joints, for example the upper extremity, i.e. the shoulder and arm joints, contains many related degrees of freedom that complicate numerical analysis. Visualisation techniques enhance the analysis process, thus improving the effectiveness of kinematic experiments. This paper describes a new visualisation system specifically designed for the analysis of multi‐joint kinematic data of the upper extremity. The challenge inherent in the data is that the upper extremity is comprised of five cooperating joints with a total of fifteen degrees of freedom. The range of motion may be affected by subtle deficiencies of individual joints that are difficult to pinpoint. To highlight these subtleties our approach combines interactive filtering and multiple visualisation techniques. Our system is further differentiated by the fact that it integrates simultaneous acquisition and visual analysis of biokinematic data. Also, to facilitate complex queries, we have designed a visual query interface with visualisation and interaction elements that are based on the domain‐specific anatomical representation of the data. The combination of these techniques form an effective approach specifically tailored for the investigation and comparison of large collections of kinematic data. This claim is supported by an evaluation experiment where the technique was used to inspect the kinematics of the left and right arm of a patient with a healed proximal humerus fracture, i.e. a healed shoulder fracture.

Abstract With ever increasing computing power, it is possible to process ever more complex fluid simulations. However, a gap between data set sizes and our ability to visualize them remains. This is especially true for the field of flow visualization, which deals with large, time‐dependent, multivariate simulation data sets. In this paper, geometry‐based flow visualization techniques form the focus of discussion. Geometric flow visualization methods place discrete objects in the velocity field whose characteristics reflect the underlying properties of the flow. A great amount of progress has been made in this field over the last two decades. However, a number of challenges remain, including placement, speed of computation and perception. In this survey, we review and classify geometric flow visualization literature according to the most important challenges when considering such a visualization, a central theme being the seeding algorithm upon which they are based. This paper details our investigation into these techniques with discussions on their applicability and their relative merits and drawbacks. The result is an up‐to‐date overview of the current state‐of‐the‐art that highlights both solved and unsolved problems in this rapidly evolving branch of research. It also serves as a concise introduction to the field of flow visualization research.

The analysis of multi-timepoint whole-body small animal CT data is greatly complicated by the varying posture of the subject at different timepoints. Due to these variations, correctly relating and comparing corresponding regions of interest is challenging.In addition, occlusion may prevent effective visualization of these regions of interest. To address these problems, we have developed a method that fully automatically maps the data to a standardized layout of sub-volumes, based on an articulated atlas registration. We have dubbed this process articulated planar reformation, or APR. A sub-volume can be interactively selected for closer inspection and can be compared with the corresponding sub-volume at the other timepoints, employing a number of different comparative visualization approaches. We provide an additional tool that highlights possibly interesting areas based on the change of bone density between timepoints. Furthermore we allow visualization of the local registration error, to give an indication of the accuracy of the registration. We have evaluated our approach on a case that exhibits cancer-induced bone resorption.

Abstract Deformation is a topic of interest in many disciplines. In particular in medical research, deformations of surfaces and even entire volumetric structures are of interest. Clear visualization of such deformations can lead to important insight into growth processes and progression of disease. We present new techniques for direct focus+context visualization of deformation fields representing transformations between pairs of volumetric datasets. Typically, such fields are computed by performing a non‐rigid registration between two data volumes. Our visualization is based on direct volume rendering and uses the GPU to compute and interactively visualize features of these deformation fields in real‐time. We integrate visualization of the deformation field with visualization of the scalar volume affected by the deformations. Furthermore, we present a novel use of texturing in volume rendered visualizations to show additional properties of the vector field on surfaces in the volume.

In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the time series is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm.We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.

We demonstrate the use of the Wii Balance Board™ as a low-cost virtual reality input device. We provide an overview of obtaining and working with the sensor input. By processing the sensor values from the balance board, we are able to use it for both discrete and continuous input, which can be used to drive a variety of VR interaction metaphors. Using continuous input, the balance board is well suited for interactions requiring two simultaneous degrees of freedom and up to three total degrees of freedom, such as navigation or rotation. The discrete input is suitable for control input, such as mode switching or object selection.

Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis'04 contest), the ionization front instability data set (Vis'08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.

Multi-field datasets contain multiple parameters defined over the same spatio-temporal domain. In medicine, such multi-field data is being used more often every day, and there is an urgent need for exploratory visualization approaches that are able to deal effectively with the data-analysis. In this paper, we present a highly interactive, coordinated view-based visualization approach that has been developed especially for dealing with multi-field medical data. It can show any number of views of the physical domain and also of the abstract high-dimensional feature space. The approach has been optimized for interactive use with very large datasets. It is based on intuitive interaction techniques, and integrates analysis techniques from pattern classification to guide the exploration process. We will give some details about the implementation, and we demonstrate the utility of our approach with two real medical use cases.

The development of domain-specific virtual reality applications is often a slow and laborious process. The integration of the domain-specific functionality in an interactive Virtual Environment requires close collaboration between domain expert and VR developer, as well as the integration of domain-specific data and software in a VR application. The software environment needs to support the entire development process and software life cycle, from the early stages of iterative, rapid prototyping to a final end-user application. In this paper, we propose the use of flexible abstraction layers in the form of a dynamic scripting language, which act as the glue between VR system components and external software libraries and applications. First, we discuss the motivation and potential of our approach, after which we overview related approaches. Then, we describe the integration of a Python interpreter in our VR toolkit. The potential of our integration approach is demonstrated by rapid prototyping features, the flexible extension of core functionality and the integration of an external toolkit. We conclude with an overview of implications our approach has for the future development of new framework features and application integration

Abstract In projection‐based Virtual Reality (VR) systems, typically only one headtracked user views stereo images rendered from the correct view position. For other users, who are presented a distorted image, moving with the first user's head motion, it is difficult to correctly view and interact with 3D objects in the virtual environment. In close‐range VR systems, such as the Virtual Workbench, distortion effects are especially large because objects are within close range and users are relatively far apart. On these systems, multi‐user collaboration proves to be difficult. In this paper, we analyze the problem and describe a novel, easy to implement method to prevent and reduce image distortion and its negative effects on close‐range interaction task performance. First, our method combines a shared camera model and view distortion compensation. It minimizes the overall distortion for each user, while important user‐personal objects such as interaction cursors, rays and controls remain distortion‐free. Second, our method retains co‐location for interaction techniques to make interaction more consistent. We performed a user experiment on our Virtual Workbench to analyze user performance under distorted view conditions with and without the use of our method. Our findings demonstrate the negative impact of view distortion on task performance and the positive effect our method introduces. This indicates that our method can enhance the multi‐user collaboration experience on close‐range, projection‐based VR systems.

A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges--arch model--is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named L and H) and thereby classify the boundary. Second, the model is used to approximate the underlying noise-free material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy.

This paper presents an approach to help speed up and unify the exploration and analysis of time-dependent, volumetric data sets by easily incorporating new qualitative and quantitative information into an exploratory virtual environment (VE). The new information is incorporated through one or more expedited offline "reprocessing" steps, which compute properties of objects extracted from the data. These objects and their properties are displayed in the exploratory VE. A case study involving atmospheric data is presented to demonstrate the utility of the method.

Computer-aided diagnosis (CAD) is a helpful addition to laborious visual inspection for preselection of suspected colonic polyps in virtual colonoscopy. Most of the previous work on automatic polyp detection makes use of indicators based on the scalar curvature of the colon wall and can result in many false-positive detections. Our work tries to reduce the number of false-positive detections in the preselection of polyp candidates. Polyp surface shape can be characterized and visualized using lines of curvature. In this paper, we describe techniques for generating and rendering lines of curvature on surfaces and we show that these lines can be used as part of a polyp detection approach. We have adapted existing approaches on explicit triangular surface meshes, and developed a new algorithm on implicit surfaces embedded in 3D volume data. The visualization of shaded colonic surfaces can be enhanced by rendering the derived lines of curvature on these surfaces. Features strongly correlated with true-positive detections were calculated on lines of curvature and used for the polyp candidate selection. We studied the performance of these features on 5 data sets that included 331 pre-detected candidates, of which 50 sites were true polyps. The winding angle had a significant discriminating power for true-positive detections, which was demonstrated by a Wilcoxon rank sum test with p < 0.001. The median winding angle and inter-quartile range (IQR) for true polyps were 7.817 and 6.770 - 9.288 compared to 2.954 and 1.995 - 3.749 for false-positive detections.

Diffusion tensor imaging (DTI) is an MRI-based technique for quantifying water diffusion in living tissue. In the white matter of the brain, water diffuses more rapidly along the neuronal axons than in the perpendicular direction. By exploiting this phenomenon, DTI can be used to determine trajectories of fiber bundles, or neuronal connections between regions, in the brain. The resulting bundles can be visualized. However, the resulting visualizations can be complex and difficult to interpret. An effective approach is to pre-determine trajectories from a large number of positions throughout the white matter (full brain fiber tracking) and to offer facilities to aid the user in selecting fiber bundles of interest. Two factors are crucial for the use and acceptance of this technique in clinical studies: firstly, the selection of the bundles by brain experts should be interactive, supported by real-time visualization of the trajectories registered with anatomical MRI scans. Secondly, the fiber selections should be reproducible, so that different experts will achieve the same results. In this paper we present a practical technique for the interactive selection of fiber-bundles using multiple convex objects that is an order of magnitude faster than similar techniques published earlier. We also present the results of a clinical study with ten subjects that show that our selection approach is highly reproducible for fractional anisotropy (FA) calculated over the selected fiber bundles.

This poster explores navigation, interaction and stereoscopic display in a multi-scale virtual space. When interaction, especially two-handed, direct manipulation, is combined with stereoscopic stationary displays, there are trade-offs that must be considered. This poster gives an overview of these design issues and briefly describes our current multi-scale, application for exploring volumetric weather in its geospatial context in a VR system. The general importance of recognizing and using defined areas of user focus and of semi-automatically bringing these areas into the optimal interaction volume of the display system is described.

Abstract Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture‐based techniques. In this paper, dense, texture‐based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio‐temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.

The quality of volume visualization depends strongly on the quality of the underlying data. In virtual colonoscopy, CT data should be acquired at a low radiation dose that results in a low signal-to-noise ratio. Alternatively, MRI data is acquired without ionizing radiation, but suffers from noise and bias (global signal fluctuations). Current volume visualization techniques often do not produce good results with noisy or biased data. This paper describes methods for volume visualization that deal with these imperfections. The techniques are based on specially adapted edge detectors using first and second order derivative filters. The filtering is integrated into the visualization process. The first order derivative method results in good quality images but suffers from localization bias. The second order method has better surface localization, especially in highly curved areas. It guarantees minimal detail smoothing resulting in a better visualization of polyps.

Abstract Flow visualisation is an attractive topic in data visualisation, offering great challenges for research. Very large data sets must be processed, consisting of multivariate data at large numbers of grid points, often arranged in many time steps. Recently, the steadily increasing performance of computers again has become a driving force for new advances in flow visualisation, especially in techniques based on texturing, feature extraction, vector field clustering, and topology extraction. In this article we present the state of the art in feature‐based flow visualisation techniques. We will present numerous feature extraction techniques, categorised according to the type of feature. Next, feature tracking and event detection algorithms are discussed, for studying the evolution of features in time‐dependent data sets. Finally, various visualisation techniques are demonstrated. ACM CSS: I.3.8 Computer Graphics— applications

In this paper we present a basic set of intuitive exploration tools for the data visualization in a Virtual Environment on the Responsive Workbench. First, we introduce the Plexipad, a transparent acrylic panel which allows two-handed interaction in combination with a stylus. After a description of various interaction scenarios with these two devices, we present a basic set of interaction tools, which support the user in the process of exploring volumetric datasets. Besides the interaction tools for navigation and selection we present tools that are closely coupled with probing tools. These interactive probing tools are used as input for complex visualization tools and for performing virtual measurements. We illustrate the use of our tools in two applications from different research areas which use volumetric and particle data.

Abstract In this paper we explore a novel combined application of two of our existing visualisation techniques to thetracking of 3D vortex tubes in an unsteady flow. The applied techniques are the winding‐angle vortex extractiontechnique based on streamline geometry, and the attribute‐based feature tracking technique. We have applied theseto the well‐known case of an unsteady 3D flow past a tapered cylinder. First, 2D vortices are detected in a number of horizontal slices for each time step, by means of the winding‐anglevortex extraction method. For each 2D vortex a number of attributes are calculated and stored. These vorticesare visualised by a special type of ellipse icons, showing the position, shape and rotational direction and speed ineach slice. Next, for each time step, 3D vortex tubes are constructed from the 2D vortices by applying the feature trackingprocedure in a spatial dimension to connect the corresponding vortices in adjacent slices. The result is a graphattribute set with the 2D vortex attributes in the nodes and the spatial correspondences as edges. Finally, the 3D vortex tubes are tracked in time using the same tracking procedure, for finding the correspondingtubes in successive time steps. The result is a description of the evolution of the 3D vortices. An interactive, time‐dependentvisualisation is generated using the temporal correspondences of each vortex tube. This analysis revealsa number of interesting patterns. ACM CSS: I.3.8 Computer Graphics—Applications

Vortices are important features in many research and engineering fields. Visualization is an important step in gaining more understanding and control of vortices. Vortex detection criteria fall into two categories: point based scalar quantities, calculated at single points, and curve based geometric criteria, calculated for, e.g., streamlines. The first category is easy to compute, but does not work in all cases. The second category is more intuitive and should work in all cases, but currently only works in 2D (or 3D projected) flows. We show applications of both approaches in hydrodynamic flows.

Surface extraction from data fields is often used in scientific visualization, as surfaces can represent meaningful information and they are well suited for display. This paper describes a general method for localized surface extraction from scalar and vector fields. An initial polygonal surface is placed within the field, and the shape of the surface is adapted to the field by iterative displacement of surface nodes according to a displacement criterion. To achieve a good approximation of a smooth surface, the polygon mesh can be locally refined during iteration. The type of surface extracted depends on the displacement criterion, which can be any function of the available field variables. Techniques for displacement and mesh refinement are discussed in detail. The results we show are generation of a local isosurface and extraction of a vortex tube. Finally, we will draw conclusions and discuss some issues for further development.

The case study describes a system that allows the use of interactive volume rendering for routine clinical diagnosis. In this setup, a SGI RealityStation acts like a remote rendering system which is controlled by a user interface that was added to an existing clinical system. The paper describes some implementation aspects, including several system optimizations that were carried out in order to optimize rendering speed. Initial results are very promising; the authors present three examples of clinical findings that were made using this system. Because of the setup, clinicians are now much more aware of the possibilities that modern hardware offers for interactive volume visualization.

We present a conceptual framework and a process model for feature extraction and iconic visualization. The features are regions of interest extracted from a dataset. They are represented by attribute sets, which play a key role in the visualization process. These attribute sets are mapped to icons, or symbolic parametric objects, for visualization. The features provide a compact abstraction of the original data, and the icons are a natural way to visualize them. We present generic techniques to extract features and to calculate attribute sets, and describe a simple but powerful modeling language which was developed to create icons and to link the attributes to the icon parameters. We present illustrative examples of iconic visualization created with the techniques described, showing the effectiveness of this approach.

Presents a conceptual framework and a process model for feature extraction and iconic visualization. Feature extraction is viewed as a process of data abstraction, which can proceed in multiple stages, and corresponding data abstraction levels. The features are represented by attribute sets, which play a key role in the visualization process. Icons are symbolic parametric objects, designed as visual representations of features. The attributes are mapped to the parameters (or degrees of freedom) of an icon. We describe some generic techniques to generate attribute sets, such as volume integrals and medial axis transforms. A simple but powerful modeling language was developed to create icons, and to link the attributes to the icon parameters. We present illustrative examples of iconic visualization created with the techniques described, showing the effectiveness of this approach.

Abstract In this paper, we present an approach to selective vector field visualization. This selective visualization approach consists of three stages: selectdon creation, selection processing and selective visualization mapping. It is described how selected regions, called selections, can be represented and created, how selections can be processed and how they can be used in the visualization mapping. Combination of these techniques with a standard visualization pipeline improves the visualization process and offers new facilities for visualization. Examples of selective visualization of fluid flow datasets are provided.

In this work a new method for visualization of three-dimensional turbulent flow using particle motion animation is presented. The method is based on Reynolds decomposition of a turbulent flow field into a convective and a turbulent motion. At each step of particle path generation a stochastic perturbation is added, resulting in random-walk motions of particles. A physical relation is established between the perturbations and the eddy-diffusivity, which is calculated in a turbulent flow simulation. The flow data used is a mean velocity field, and an eddy-diffusivity field. The erratic particle motions are more than just a visual effect, but represent a real physical phenomenon. An implementation of the method is described, and an example of a turbulent channel flow is given, which clearly shows the random particle motions in their context of general fluid motion patterns.>

Deformations, such as bending, twisting and tapering, are important operations for modifying the shape of solid objects. In this paper, we present methods to deform objects defined by sweeping. A closed 2D polygon (the contour) moves along a 3D polyline (the trajectory), thus producing a polygonal approximation of a generalized cylinder. During sweeping, the contour can be incrementally scaled and rotated, resulting in tapered and twisted objects. Also, smoothly bent objects can be produced by performing local rounding operations on trajectories. The methods have been implemented in the solid modeller PLAMO, in which the deformed sweep objects can be used, as well as other types of bodies, as primitives in a CSG tree.