DevLan/vispubs huggingface dataset:
Illustrative White Matter Fiber Bundles
Authors: Ron Otten, Anna Vilanova, Huub van de Wetering
Published at EuroVis, 2010

Diffusion Tensor Imaging (DTI) has made feasible the visualization of the fibrous structure of the brain white matter. In the last decades, several fiber‐tracking methods have been developed to reconstruct the fiber tracts from DTI data. Usually these fiber tracts are shown individually based on some selection criteria like region of interest. However, if the white matter as a whole is being visualized clutter is generated by directly rendering the individual fiber tracts. Often users are actually interested in fiber bundles, anatomically meaningful entities that abstract from the fibers they contain. Several clustering techniques have been developed that try to group the fiber tracts in fiber bundles. However, even if clustering succeeds, the complex nature of white matter still makes it difficult to investigate. In this paper, we propose the use of illustration techniques to ease the exploration of white matter clusters. We create a technique to visualize an individual cluster as a whole. The amount of fibers visualized for the cluster is reduced to just a few hint lines, and silhouette and contours are used to improve the definition of the cluster borders. Multiple clusters can be easily visualized by a combination of the single cluster visualizations. Focus+context concepts are used to extend the multiple‐cluster renderings. Exploded views ease the exploration of the focus cluster while keeping the context clusters in an abstract form. Real‐time results are achieved by the GPU implementation of the presented techniques.
Image-Based Edge Bundles: Simplified Visualization of Large Graphs
Authors: Alexandru C. Telea, Ozan Ersoy
Published at EuroVis, 2010

We present a new approach aimed at understanding the structure of connections in edge‐bundling layouts. We combine the advantages of edge bundles with a bundle‐centric simplified visual representation of a graph's structure. For this, we first compute a hierarchical edge clustering of a given graph layout which groups similar edges together. Next, we render clusters at a user‐selected level of detail using a new image‐based technique that combines distance‐based splatting and shape skeletonization. The overall result displays a given graph as a small set of overlapping shaded edge bundles. Luminance, saturation, hue, and shading encode edge density, edge types, and edge similarity. Finally, we add brushing and a new type of semantic lens to help navigation where local structures overlap. We illustrate the proposed method on several real‐world graph datasets.
Isosurface Similarity Maps
Authors: Stefan Bruckner, Torsten Möller
Published at EuroVis, 2010

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.
Matrix Trees
Authors: Nathan Andrysco, Xavier Tricoche
Published at EuroVis, 2010

We propose a new data representation for octrees and kd‐trees that improves upon memory size and algorithm speed of existing techniques. While pointerless approaches exploit the regular structure of the tree to facilitate efficient data access, their memory footprint becomes prohibitively large as the height of the tree increases. Pointerbased trees require memory consumption proportional to the number of tree nodes, thus exploiting the typical sparsity of large trees. Yet, their traversal is slowed by the need to follow explicit pointers across the different levels. Our solution is a pointerless approach that represents each tree level with its own matrix, as opposed to traditional pointerless trees that use only a single vector. This novel data organization allows us to fully exploit the tree's regular structure and improve the performance of tree operations. By using a sparse matrix data structure we obtain a representation that is suited for sparse and dense trees alike. In particular, it uses less total memory than pointer‐based trees even when the data set is extremely sparse. We show how our approach is easily implemented on the GPU and illustrate its performance in typical visualization scenarios.
Multi-layer Depth Peeling by Single-Pass Rasterisation for Faster Isosurface Raytracing on GPUs
Authors: Baoquan Liu, Gordon Clapworthy, Feng Dong 0005
Published at EuroVis, 2010

Empty‐space skipping is an essential acceleration technique for volume rendering. Image‐order empty‐space skipping is not well suited to GPU implementation, since it must perform checks on, essentially, a per‐sample basis, as in kd‐tree traversal, which can lead to a great deal of divergent branching at runtime, which is very expensive in a modern GPU pipeline. In contrast, object‐order empty‐space skipping is extremely fast on a GPU and has negligible overheads compared with approaches without empty‐space skipping, since it employs the hardware unit for rasterisation. However, previous object‐order algorithms have been able to skip only exterior empty space and not the interior empty space that lies inside or between volume objects.In this paper, we address these issues by proposing a multi‐layer depth‐peeling approach that can obtain all of the depth layers of the tight‐fitting bounding geometry of the isosurface by a single rasterising pass. The maximum count of layers peeled by our approach can be up to thousands, while maintaining 32‐bit float‐point accuracy, which was not possible previously. By raytracing only the valid ray segments between each consecutive pair of depth layers, we can skip both the interior and exterior empty space efficiently.In comparisons with 3 state‐of‐the‐art GPU isosurface rendering algorithms, this technique achieved much faster rendering across a variety of data sets.
Non-iterative Second-order Approximation of Signed Distance Functions for Any Isosurface Representation
Authors: Vladimir Molchanov, Paul Rosenthal, Lars Linsen
Published at EuroVis, 2010

Signed distance functions (SDF) to explicit or implicit surface representations are intensively used in various computer graphics and visualization algorithms. Among others, they are applied to optimize collision detection, are used to reconstruct data fields or surfaces, and, in particular, are an obligatory ingredient for most level set methods. Level set methods are common in scientific visualization to extract surfaces from scalar or vector fields. Usual approaches for the construction of an SDF to a surface are either based on iterative solutions of a special partial differential equation or on marching algorithms involving a polygonization of the surface. We propose a novel method for a non‐iterative approximation of an SDF and its derivatives in a vicinity of a manifold. We use a second‐order algebraic fitting scheme to ensure high accuracy of the approximation. The manifold is defined (explicitly or implicitly) as an isosurface of a given volumetric scalar field. The field may be given at a set of irregular and unstructured samples. Stability and reliability of the SDF generation is achieved by a proper scaling of weights for the Moving Least Squares approximation, accurate choice of neighbors, and appropriate handling of degenerate cases. We obtain the solution in an explicit form, such that no iterative solving is necessary, which makes our approach fast.
Out-of-Core Simplification and Crack-Free LOD Volume Rendering for Irregular Grids
Authors: Zhiyan Du, Yi-Jen Chiang
Published at EuroVis, 2010

We propose a novel out‐of‐core simplification and level‐of‐detail (LOD) volume rendering algorithm for large irregular grids represented as tetrahedral meshes. One important feature of our algorithm is that it creates a space decomposition as required by I/O‐efficient simplification and volume rendering, and simplifies both the internal and boundary portions of the sub‐volumes progressively by edge collapses using the (extended) quadric error metric, while ensuring any selected LOD mesh to be crack‐free (i.e., any neighboring sub‐volumes in the LOD have consistent boundaries, and all the cells in the LOD do not have negative volumes), with all computations performed I/O‐ejficiently. This has been an elusive goal for out‐of‐core progressive meshes and LOD visualization, and our novel solution achieves this goal with a theoretical guarantee to be crack‐free for tetrahedral meshes. As for selecting a desirable LOD mesh for volume rendering, our technique supports selective refinement LODs (where different places can have different error bounds), in addition to the basic uniform LODs (where the error bound is the same in all places). The proposed scalar‐value range and view‐dependent selection queries for selective refinement are especially effective in producing images of the highest quality with a much faster rendering speed. The experiments demonstrate the efficacy of our new technique.
Pathline: A Tool For Comparative Functional Genomics
Authors: Miriah D. Meyer, Bang Wong, Mark P. Styczynski, Tamara Munzner, Hanspeter Pfister
Published at EuroVis, 2010

Biologists pioneering the new field of comparative functional genomics attempt to infer the mechanisms of gene regulation by looking for similarities and differences of gene activity over time across multiple species. They use three kinds of data: functional data such as gene activity measurements, pathway data that represent a series of reactions within a cellular process, and phylogenetic relationship data that describe the relatedness of species. No existing visualization tool can visually encode the biologically interesting relationships between multiple pathways, multiple genes, and multiple species. We tackle the challenge of visualizing all aspects of this comparative functional genomics dataset with a new interactive tool called Pathline. In addition to the overall characterization of the problem and design of Pathline, our contributions include two new visual encoding techniques. One is a new method for linearizing metabolic pathways that provides appropriate topological information and supports the comparison of quantitative data along the pathway. The second is the curvemap view, a depiction of time series data for comparison of gene activity and metabolite levels across multiple species. Pathline was developed in close collaboration with a team of genomic scientists. We validate our approach with case studies of the biologists’ use of Pathline and report on how they use the tool to confirm existing findings and to discover new scientific insights.
ProbExplorer: Uncertainty-guided Exploration and Editing of Probabilistic Medical Image Segmentation
Authors: Ahmed Saad, Torsten Möller, Ghassan Hamarneh
Published at EuroVis, 2010

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.
Real-Time Temporal-Coherent Color Contrast Enhancement for Dichromats
Authors: Gustavo Mello Machado, Manuel M. Oliveira
Published at EuroVis, 2010

We present an automatic image‐recoloring technique for enhancing color contrast for dichromats whose computational cost varies linearly with the number of input pixels. Our approach can be efficiently implemented on GPUs, and we show that for typical image sizes it is up to two orders of magnitude faster than the current state‐of‐the‐art technique. Unlike previous approaches, ours preserve temporal coherence and, therefore, is suitable for video recoloring. We demonstrate the effectiveness of our technique by integrating it into a visualization system and showing, for the first time, real‐time high‐quality recolored visualizations for dichromats.
Reusable Visualizations and Animations for Surgery Planning
Authors: Konrad Mühler, Bernhard Preim
Published at EuroVis, 2010

For surgical planning, the exploration of 3D visualizations and 2D slice views is essential. However, the generation of visualizations which support the specific treatment decisions is very tedious. Therefore, the reuse of once designed visualizations for similar cases can strongly accelerate the process of surgical planning. We present a new technique that enables the easy reuse of both medical visualization types: 3D scenes and 2D slice views. We introduce the keystates as a concept to describe the state of a visualization in a general manner. They can be easily applied to new datasets to create similar visualizations. Keystates can be shared between surgeons of one specialization to reproduce and document the planning process for collaborative work. Furthermore, animations can support the surgeon on individual exploration and are also useful in collaborative environments, where complex issues must be presented in a short time. Therefore, we provide a framework, where animations can be visually designed by surgeons during their exploration process without any programming or authoring skills. We discuss several transitions between different visualizations and present an application from clinical routine.
Scalable Multi-view Registration for Multi-Projector Displays on Vertically Extruded Surfaces
Authors: Behzad Sajadi, Aditi Majumder
Published at EuroVis, 2010

Recent work have shown that it is possible to register multiple projectors on non‐planar surfaces using a single uncalibrated camera instead of a calibrated stereo pair when dealing with a special class of non‐planar surfaces, vertically extruded surfaces. However, this requires the camera view to contain the entire display surface. This is often an impossible scenario for large displays, especially common in visualization, edutainment, training and simulation applications. In this paper we present a new method that can achieve an accurate geometric registration even when the field‐of‐view of the uncalibrated camera can cover only a part of the vertically extruded display at a time. We pan and tilt the camera from a single point and employ a multi‐view approach to register the projectors on the display. This allows the method to scale easily both in terms of camera resolution and display size. To the best of our knowledge, our method is the first to achieve a scalable multi‐view geometric registration of large vertically extruded displays with a single uncalibrated camera. This method can also handle a different situation of having multiple similarly oriented cameras in different locations, if the camera focal length is known.
SmallWorlds: Visualizing Social Recommendations
Authors: Brynjar Gretarsson, John O'Donovan, Svetlin Bostandjiev, Christopher Hall 0001, Tobias Höllerer
Published at EuroVis, 2010

We present SmallWorlds, a visual interactive graph‐based interface that allows users to specify, refine and build item‐preference profiles in a variety of domains. The interface facilitates expressions of taste through simple graph interactions and these preferences are used to compute personalized, fully transparent item recommendations for a target user. Predictions are based on a collaborative analysis of preference data from a user's direct peer group on a social network. We find that in addition to receiving transparent and accurate item recommendations, users also learn a wealth of information about the preferences of their peers through interaction with our visualization. Such information is not easily discoverable in traditional text based interfaces. A detailed analysis of our design choices for visual layout, interaction and prediction techniques is presented. Our evaluations discuss results from a user study in which SmallWorlds was deployed as an interactive recommender system on Facebook.
Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns
Authors: Gennady L. Andrienko, Natalia V. Andrienko, Sebastian Bremm, Tobias Schreck, Tatiana von Landesberger, Peter Bak, Daniel A. Keim
Published at EuroVis, 2010

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post‐processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two‐dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA.
Streaming-Enabled Parallel Dataflow Architecture for Multicore Systems
Authors: Huy T. Vo, Daniel K. Osmari, Brian Summa, João Luiz Dihl Comba, Valerio Pascucci, Cláudio T. Silva
Published at EuroVis, 2010

We propose a new framework design for exploiting multi‐core architectures in the context of visualization dataflow systems. Recent hardware advancements have greatly increased the levels of parallelism available with all indications showing this trend will continue in the future. Existing visualization dataflow systems have attempted to take advantage of these new resources, though they still have a number of limitations when deployed on shared memory multi‐core architectures. Ideally, visualization systems should be built on top of a parallel dataflow scheme that can optimally utilize CPUs and assign resources adaptively to pipeline elements. We propose the design of a flexible dataflow architecture aimed at addressing many of the shortcomings of existing systems including a unified execution model for both demand‐driven and event‐driven models; a resource scheduler that can automatically make decisions on how to allocate computing resources; and support for more general streaming data structures which include unstructured elements. We have implemented our system on top of VTK with backward compatibility. In this paper, we provide evidence of performance improvements on a number of applications.
Supporting Exploratory Analysis with the Select & Slice Table
Authors: Yedendra Babu Shrinivasan, Jarke J. van Wijk
Published at EuroVis, 2010

In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select &amp; Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select &amp; Slice table during exploratory analysis of multivariate data.
The Perception of Correlation in Scatterplots
Authors: Ronald A. Rensink, Gideon Baldridge
Published at EuroVis, 2010

We present a rigorous way to evaluate the visual perception of correlation in scatterplots, based on classical psychophysical methods originally developed for simple properties such as brightness. Although scatterplots are graphically complex, the quantity they convey is relatively simple. As such, it may be possible to assess the perception of correlation in a similar way.Scatterplots were each of 5.0° extent, containing 100 points with a bivariate normal distribution. Means were 0.5 of the range of the points, and standard deviations 0.2 of this range. Precision was determined via an adaptive algorithm to find the just noticeable differences (jnds) in correlation, i.e., the difference between two side‐by‐side scatterplots that could be discriminated 75% of the time. Accuracy was measured by direct estimation, using reference scatterplots with fixed upper and lower values, with a test scatterplot adjusted so that its correlation appeared to be halfway between these. This process was recursively applied to yield several further estimates.Results of the discrimination tests show jnd(r) = k (1/b – r), where r is the Pearson correlation, and parameters 0 &lt; k, b &lt; 1. Integration yields a subjective estimate of correlation g(r) = ln(1 – br) / ln(1 – b). The values of b found via discrimination closely match those found via direct estimation. As such, it appears that the perception of correlation in a scatterplot is completely described by two related performance curves, specified by two easily‐measured parameters.