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Item Enhancing the Interactive Visualization of Procedurally Encoded Multifield Data with Ellipsoidal Basis Functions(The Eurographics Association and Blackwell Publishing, Inc, 2006) Jang, Yun; Botchen, Ralf P.; Lauser, Andreas; Ebert, David S.; Gaither, Kelly P.; Ertl, ThomasFunctional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics, including surface, volume, and vector datasets. Typically, sums of Gaussians or similar radial basis functions are used in the functional approximation and PC graphics hardware is used to quickly evaluate and render these datasets. Previously, researchers presented techniques for spatially-limited spherical Gaussian radial basis function encoding and visualization of volumetric scalar, vector, and multifield datasets. While truncated radially symmetric basis functions are quick to evaluate and simple for encoding optimization, they are not the most appropriate choice for data that is not radially symmetric and are especially problematic for representing linear, planar, and many non-spherical structures. Therefore, we have developed a volumetric approximation and visualization system using ellipsoidal Gaussian functions which provides greater compression, and visually more accurate encodings of volumetric scattered datasets. In this paper, we extend previous work to use ellipsoidal Gaussians as basis functions, create a rendering system to adapt these basis functions to graphics hardware rendering, and evaluate the encoding effectiveness and performance for both spherical Gaussians and ellipsoidal Gaussians.Categories and Subject Descriptors (according to ACMCCS): I.3.3 [Computer Graphics]: Scientific Visualization, Ellipsoidal Basis Functions, Functional Approximation, Texture AdvectionItem Shape Context Preserving Deformation of 2D Anatomical Illustrations(The Eurographics Association and Blackwell Publishing Ltd, 2009) Chen, Wei; Liang, Xiao; Maciejewski, Ross; Ebert, David S.In this paper, we present a novel two-dimensional (2D) shape context preserving image manipulation approach which constructs and manipulates a 2D mesh with a new differential mesh editing algorithm. We introduce a novel shape context descriptor and integrate it into the deformation framework, facilitating shape-preserving deformation for 2D anatomical illustrations. Our new scheme utilizes an analogy based shape transfer technique in order to learn shape styles from reference images. Experimental results show that visually plausible deformation can be quickly generated from an existing example at interactive frame rates. An experienced artist has evaluated our approach and his feedback is quite encouraging.Item Bivariate Transfer Functions on Unstructured Grids(The Eurographics Association and Blackwell Publishing Ltd., 2009) Song, Yuyan; Chen, Wei; Maciejewski, Ross; Gaither, Kelly P.; Ebert, David S.; H.-C. Hege, I. Hotz, and T. MunznerMulti-dimensional transfer functions are commonly used in rectilinear volume renderings to effectively portray materials, material boundaries and even subtle variations along boundaries. However, most unstructured grid rendering algorithms only employ one-dimensional transfer functions. This paper proposes a novel pre-integrated Projected Tetrahedra (PT) rendering technique that applies bivariate transfer functions on unstructured grids. For each type of bivariate transfer function, an analytical form that pre-integrates the contribution of a ray segment in one tetrahedron is derived, and can be precomputed as a lookup table to compute the color and opacity in a projected tetrahedron on-the-fly. Further, we show how to approximate the integral using the pre-integration method for faster unstructured grid rendering. We demonstrate the advantages of our approach with a variety of examples and comparisons with one-dimensional transfer functions.Item Shape-aware Volume Illustration(The Eurographics Association and Blackwell Publishing Ltd, 2007) Chen, Wei; Lu, Aidong; Ebert, David S.We introduce a novel volume illustration technique for regularly sampled volume datasets. The fundamental difference between previous volume illustration algorithms and ours is that our results are shape-aware, as they depend not only on the rendering styles, but also the shape styles. We propose a new data structure that is derived from the input volume and consists of a distance volume and a segmentation volume. The distance volume is used to reconstruct a continuous field around the object boundary, facilitating smooth illustrations of boundaries and silhouettes. The segmentation volume allows us to abstract or remove distracting details and noise, and apply different rendering styles to different objects and components. We also demonstrate how to modify the shape of illustrated objects using a new 2D curve analogy technique. This provides an interactive method for learning shape variations from 2D hand-painted illustrations by drawing several lines. Our experiments on several volume datasets demonstrate that the proposed approach can achieve visually appealing and shape-aware illustrations. The feedback from medical illustrators is quite encouraging.Item Context-aware Volume Modeling of Skeletal Muscles(The Eurographics Association and Blackwell Publishing Ltd., 2009) Yan, Zhicheng; Chen, Wei; Lu, Aidong; Ebert, David S.; H.-C. Hege, I. Hotz, and T. MunznerThis paper presents an interactive volume modeling method that constructs skeletal muscles from an existing volumetric dataset. Our approach provides users with an intuitive modeling interface and produces compelling results that conform to the characteristic anatomy in the input volume. The algorithmic core of our method is an intuitive anatomy classification approach, suited to accommodate spatial constraints on the muscle volume. The presented work is useful in illustrative visualization, volumetric information fusion and volume illustration that involve muscle modeling, where the spatial context should be faithfully preserved.Item Abstractive Representation and Exploration of Hierarchically Clustered Diffusion Tensor Fiber Tracts(The Eurographics Association and Blackwell Publishing Ltd., 2008) Chen, Wei; Zhang, Song; Correia, Stephen; Ebert, David S.; A. Vilanova, A. Telea, G. Scheuermann, and T. MoellerDiffusion tensor imaging (DTI) has been used to generate fibrous structures in both brain white matter and muscles. Fiber clustering groups the DTI fibers into spatially and anatomically related tracts. As an increasing number of fiber clustering methods have been recently developed, it is important to display, compare, and explore the clustering results efficiently and effectively. In this paper, we present an anatomical visualization technique that reduces the geometric complexity of the fiber tracts and emphasizes the high-level structures. Beginning with a volumetric diffusion tensor image, we first construct a hierarchical clustering representation of the fiber bundles. These bundles are then reformulated into a 3D multi-valued volume data. We then build a set of geometric hulls and principal fibers to approximate the shape and orientation of each fiber bundle. By simultaneously visualizing the geometric hulls, individual fibers, and other data sets such as fractional anisotropy, the overall shape of the fiber tracts are highlighted, while preserving the fibrous details. A rater with expert knowledge of white matter structure has evaluated the resulting interactive illustration and confirmed the improvement over straightforward DTI fiber tract visualization.Item SDViz: A Context-Preserving Interactive Visualization System for Technical Diagrams(The Eurographics Association and Blackwell Publishing Ltd., 2009) Woo, Insoo; Kim, SungYe; Maciejewski, Ross; Ebert, David S.; Ropp, Timothy D.; Thomas, Krystal; H.-C. Hege, I. Hotz, and T. MunznerWhen performing daily maintenance and repair tasks, technicians require access to a variety of technical diagrams. As technicians trace components and diagrams from page-to-page, within and across manuals, the contextual information of the components they are analyzing can easily be lost. To overcome these issues, we have developed a Schematic Diagram Visualization System (SDViz) designed for maintaining and highlighting contextual information in technical documents, such as schematic and wiring diagrams. Our system incorporates various features to aid in the navigation and diagnosis of faults, as well as maintaining contextual information when tracing components/connections through multiple diagrams. System features include highlighting relationships between components and connectors, diagram annotation tools, the animation of flow through the system, a novel contextual blending method, and a variety of traditional focus+context visualization techniques. We have evaluated the usefulness of our system through a qualitative user study in which subjects utilized our system in diagnosing faults during a standard aircraft maintenance exercise.