Authors
Ove Daae Lampe
Carlos Correa
Kwan-Liu Ma
Helwig Hauser

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.136

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Abstract
We present two visualization techniques for curve-centric volume reformation with the aim to create compelling comparative visualizations. A curve-centric volume reformation deforms a volume, with regards to a curve in space, to create a new space in which the curve evaluates to zero in two dimensions and spans its arc-length in the third. The volume surrounding the curve is deformed such that spatial neighborhood to the curve is preserved. The result of the curve-centric reformation produces images where one axis is aligned to arc-length, and thus allows researchers and practitioners to apply their arc-length parameterized data visualizations in parallel for comparison. Furthermore we show that when visualizing dense data, our technique provides an inside out projection, from the curve and out into the volume, which allows for inspection what is around the curve. Finally we demonstrate the usefulness of our techniques in the context of two application cases. We show that existing data visualizations of arc-length parameterized data can be enhanced by using our techniques, in addition to creating a new view and perspective on volumetric data around curves. Additionally we show how volumetric data can be brought into plotting environments that allow precise readouts. In the first case we inspect streamlines in a flow field around a car, and in the second we inspect seismic volumes and well logs from
drilling.

Authors
Tiago Etiene
Carlos Scheidegger
L. Gustavo Nonato
Robert M. Kirby
Cláudio T. Silva

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.194

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Abstract
Visual representations of isosurfaces are ubiquitous in the scientific and engineering literature. In this paper, we present techniques to assess the behavior of isosurface extraction codes. Where applicable, these techniques allow us to distinguish whether anomalies in isosurface features can be attributed to the underlying physical process or to artifacts from the extraction process. Such scientific scrutiny is at the heart of verifiable visualization – subjecting visualization algorithms to the same verification process that is used in other components of the scientific pipeline. More concretely, we derive formulas for the expected order of accuracy (or convergence rate) of several isosurface features, and compare them to experimentally observed results in the selected codes. This technique is practical: in two cases, it exposed actual problems in implementations. We provide the reader with the range of responses they can expect to encounter with isosurface techniques, both under “normal operating conditions” and also under adverse conditions. Armed with this information – the results of the verification process – practitioners can judiciously select the isosurface extraction technique appropriate for their problem of interest, and have confidence in its behavior.

Authors
Andrew S. Forsberg
Jian Chen
David H. Laidlaw

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.126

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Abstract
In a user study comparing four visualization methods for three-dimensional vector data, participants used visualizations from each method to perform five simple but representative tasks: 1) determining whether a given point was a critical point, 2) determining the type of a critical point, 3) determining whether an integral curve would advect through two points, 4) determining whether swirling movement is present at a point, and 5) determining whether the vector field is moving faster at one point than another. The visualization methods were line and tube representations of integral curves with both monoscopic and stereoscopic viewing. While participants reported a preference for stereo lines, quantitative results showed performance among the tasks varied by method. Users performed all tasks better with methods that: 1) gave a clear representation with no perceived occlusion, 2) clearly visualized curve speed and direction information, and 3) provided fewer rich 3D cues (e.g., shading, polygonal arrows, overlap cues, and surface textures). These results provide quantitative support for anecdotal evidence on visualization methods. The tasks and testing framework also give a basis for comparing other visualization methods, for creating more effective methods, and for defining additional tasks to explore further the tradeoffs among the methods.

Authors
Jibonananda Sanyal
Song Zhang
Gargi Bhattacharya
Phil Amburn
Robert J. Moorhead

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.114

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Abstract
Many techniques have been proposed to show uncertainty in data visualizations. However, very little is known about their effectiveness in conveying meaningful information. In this paper, we present a user study that evaluates the perception of uncertainty amongst four of the most commonly used techniques for visualizing uncertainty in one-dimensional and two-dimensional data. The techniques evaluated are traditional errorbars, scaled size of glyphs, color-mapping on glyphs, and color-mapping of uncertainty on the data surface. The study uses generated data that was designed to represent the systematic and random uncertainty components. Twenty-seven users performed two types of search tasks and two types of counting tasks on 1D and 2D datasets. The search tasks involved finding data points that were least or most uncertain. The counting tasks involved counting data features or uncertainty features. A 4×4 full-factorial ANOVA indicated a significant interaction between the techniques used and the type of tasks assigned for both datasets indicating that differences in performance between the four techniques depended on the type of task performed. Several one-way ANOVAs were computed to explore the simple main effects. Bonferronni’s correction was used to control for the family-wise error rate for alpha-inflation. Although we did not find a consistent order among the four techniques for all the tasks, there are several findings from the study that we think are useful for uncertainty visualization design. We found a significant difference in user performance between searching for locations of high and searching for locations of low uncertainty. Errorbars consistently underperformed throughout the experiment. Scaling the size of glyphs and color-mapping of the surface performed reasonably well. The efficiency of most of these techniques were highly dependent on the tasks performed. We believe that these findings can be used in future uncertainty visualization design. In addition, the framework developed in this user study presents a structured approach to evaluate uncertainty visualization techniques, as well as provides a basis for future research in uncertainty visualization.

Authors
Gregory Cipriano
George N. Phillips Jr.
Michael Gleicher

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.168

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Abstract
Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis.

Authors
Guangyu Zou
Jing Hua
Zhaoqiang Lai
Xianfeng Gu
Ming Dong

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.159

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Abstract
This paper formalizes a novel, intrinsic geometric scale space (IGSS) of 3D surface shapes. The intrinsic geometry of a surface is diffused by means of the Ricci ?ow for the generation of a geometric scale space. We rigorously prove that this multiscale shape representation satis?es the axiomatic causality property. Within the theoretical framework, we fur ther present a feature-based shape representation derived from IGSS processing, which is shown to be theoretically plausible and practically effective. By integrating the concept of scale-dependent saliency into the shape description, this representation is not only highly descriptive of the local structures, but also exhibits several desired characteristics of global shape representations, such as being compact, robust to noise and computationally ef?cient. We demonstrate the capabilities of our approach through salient geometric feature detection and highly discriminative matching of 3D scans.

Authors
Shigeo Takahashi
Issei Fujishiro
Masato Okada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.119

Abstract
A contour tree is a powerful tool for delineating the topological evolution of isosurfaces of a single-valued function, and thus has been frequently used as a means of extracting features from volumes and their time-varying behaviors. Several sophisticated algorithms have been proposed for constructing contour trees while they often complicate the software implementation especially for higher-dimensional cases such as time-varying volumes. This paper presents a simple yet effective approach to plotting in 3D space, approximate contour trees from a set of scattered samples embedded in the high-dimensional space. Our main idea is to take advantage of manifold learning so that we can elongate the distribution of high-dimensional data samples to embed it into a low-dimensional space while respecting its local proximity of sample points. The contribution of this paper lies in the introduction of new distance metrics to manifold learning, which allows us to reformulate existing algorithms as a variant of currently available dimensionality reduction scheme. Efficient reduction of data sizes together with segmentation capability is also developed to equip our approach with a coarse-to-fine analysis even for large-scale datasets. Examples are provided to demonstrate that our proposed scheme can successfully traverse the features of volumes and their temporal behaviors through the constructed contour trees.

Authors
Julien Tierny
Attila Gyulassy
Eddie Simon
Valerio Pascucci

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.163

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Abstract
This paper introduces an efficient algorithm for computing the Reeb graph of a scalar function f defined on a volumetric mesh M in R^3. We introduce a procedure called “loop surgery” that transforms M into a mesh M’ by a sequence of cuts and guarantees the Reeb graph of f(M’) to be loop free. Therefore, loop surgery reduces Reeb graph computation to the simpler problem of computing a contour tree, for which well-known algorithms exist that are theoretically efficient (O(n log n)) and fast in practice. Inverse cuts reconstruct the loops removed at the beginning.

The time complexity of our algorithm is that of a contour tree computation plus a loop surgery overhead, which depends on the number of handles of the mesh. Our systematic experiments confirm that for real-life data, this overhead is comparable to the computation of the contour tree, demonstrating virtually linear scalability on meshes ranging from 70 thousand to 3.5 million tetrahedra. Performance numbers show that our algorithm, although restricted to volumetric data, has an average speedup factor of 6,500 over the previous fastest techniques, handling larger and more complex data-sets.

We demonstrate the verstility of our approach by extending fast topologically clean isosurface extraction to non simply-connected domains. We apply this technique in the context of pressure analysis for mechanical design. In this case, our technique produces results in matter of seconds even for the largest meshes. For the same models, previous Reeb graph techniques do not produce a result.