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Now showing 1 - 10 of 10
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    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, Thomas
    Functional 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 Advection
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    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.
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    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. Munzner
    Multi-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.
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    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.
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    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. Munzner
    This 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.
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    A Visual Analytics Approach to Facilitate Crime Hotspot Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Neto, José F. de Queiroz; Santos, Emanuele; Vidal, Creto Augusto; Ebert, David S.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Computer-based technology has played a significant role in crime prevention over the past 30 years, especially with the popularization of spatial databases and crime mapping systems. Police departments frequently use hotspot analysis to identify regions that should be a priority in receiving preventive resources. Practitioners and researchers agree that tracking crime over time and identifying its geographic patterns are vital information for planning efficiently. Frequently, police departments have access to systems that are too complicated and excessively technical, leading to modest usage. By working closely together with domain experts from police agencies of two different countries, we identified and characterized five domain tasks inherent to the hotspot analysis problem and developed SHOC, a visualization tool that strives for simplicity and ease of use in helping users to perform all the domain tasks. SHOC is included in a visual analytics system that allows users without technical expertise to annotate, save, and share analyses. We also demonstrate that our system effectively supports the completion of the domain tasks in two different real-world case studies.
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    MarketAnalyzer: An Interactive Visual Analytics System for Analyzing Competitive Advantage Using Point of Sale Data
    (The Eurographics Association and Blackwell Publishing Ltd., 2012) Ko, Sungahn; Maciejewski, Ross; Jang, Yun; Ebert, David S.; S. Bruckner, S. Miksch, and H. Pfister
    Competitive intelligence is a systematic approach for gathering, analyzing, and managing information to make informed business decisions. Many companies use competitive intelligence to identify risks and opportunities within markets. Point of sale data that retailers share with vendors is of critical importance in developing competitive intelligence. However, existing tools do not easily enable the analysis of such large and complex data. therefore, new approaches are needed in order to facilitate better analysis and decision making. In this paper, we present MarketAnalyzer, an interactive visual analytics system designed to allow vendors to increase their competitive intelligence. MarketAnalyzer utilizes pixel-based matrices to present sale data, trends, and market share growths of products of the entire market within a single display. These matrices are augmented by advanced underlying analytical methods to enable the quick evaluation of growth and risk within market sectors. Furthermore, our system enables the aggregation of point of sale data in geographical views that provide analysts with the ability to explore the impact of regional demographics and trends. Additionally, overview and detailed information is provided through a series of coordinated multiple views. In order to demonstrate the effectiveness of our system, we provide two use-case scenarios as well as feedback from market analysts.
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    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. Moeller
    Diffusion 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.
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    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. Munzner
    When 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.
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    A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Zhang, Jiawei; Ahlbrand, Benjamin; Malik, Abish; Chae, Junghoon; Min, Zhiyu; Ko, Sungahn; Ebert, David S.; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
    Real-time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non-trivial task. To this end, we present a visual analytics situational awareness environment that supports the real-time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine-grained, crisis-related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph-based visual design. We propose a transparency-based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.