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Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration

  • Book
  • © 2009

Overview

Editors:
  1. Torsten Möller

    1. School of Computing Science, Simon Fraser University, Burnaby, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Bernd Hamann

    1. Department of Computer Science, University of California, Davis, Davis, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Robert D. Russell

    1. School of Computing Science, Simon Fraser University, Burnaby, Canada

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    You can also search for this editor in PubMed   Google Scholar

  • Cutting-edge research in a fast-moving cross-disciplinary field touching visualization, computer graphics, and CSE
  • Includes supplementary material: sn.pub/extras

Part of the book series: Mathematics and Visualization (MATHVISUAL)

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Table of contents (18 chapters)

  1. Front Matter

    Pages 1-8
    Download chapter PDF

  2. Maximizing Adaptivity in Hierarchical Topological Models Using Cancellation Trees

    • Peer-Timo Bremer, Valerio Pascucci, Bernd Hamann
    Pages 1-18

  3. The TOPORRERY: computation and presentation of multi-resolution topology

    • Valerio Pascucci, Kree Cole-McLaughlin, Giorgio Scorzelli
    Pages 19-40

  4. Isocontour based Visualization of Time-varying Scalar Fields

    • Ajith Mascarenhas, Jack Snoeyink
    Pages 41-68

  5. DeBruijn Counting for Visualization Algorithms

    • David C. Banks, Paul K. Stockmeyer
    Pages 69-88

  6. Topological Methods for Visualizing Vortical Flows

    • Xavier Tricoche, Christoph Garth
    Pages 89-107

  7. Stability and Computation of Medial Axes - a State-of-the-Art Report

    • Dominique Attali, Jean-Daniel Boissonnat, Herbert Edelsbrunner
    Pages 109-125

  8. Local Geodesic Parametrization: an Ant’s Perspective

    • Lior Shapira, Ariel Shamir
    Pages 127-137

  9. Tensor-Fields Visualization Using a Fabric-like Texture Applied to Arbitrary Two-dimensional Surfaces

    • Ingrid Hotz, Louis Feng, Bernd Hamann, Kenneth Joy
    Pages 139-155

  10. Flow Visualization via Partial Differential Equations

    • Tobias Preusser, Martin Rumpf, Alex Telea
    Pages 157-189

  11. Iterative Twofold Line Integral Convolution for Texture-Based Vector Field Visualization

    • Daniel Weiskopf
    Pages 191-211

  12. Constructing 3D Elliptical Gaussians for Irregular Data

    • Wei Hong, Neophytos Neophytou, Klaus Mueller, Arie Kaufman
    Pages 213-225

  13. From Sphere Packing to the Theory of Optimal Lattice Sampling

    • Alireza Entezari, Ramsay Dyer, Torsten Möller
    Pages 227-255

  14. Reducing Interpolation Artifacts by Globally Fairing Contours

    • Martin Bertram, Hans Hagen
    Pages 257-269

  15. Time- and Space-efficient Error Calculation for Multiresolution Direct Volume Rendering

    • Attila Gyulassy, Lars Linsen, Bernd Hamann
    Pages 271-283

  16. Massive Data Visualization: A Survey

    • Kenneth I. Joy
    Pages 285-302

  17. Compression and Occlusion Culling for Fast Isosurface Extraction from Massive Datasets

    • Benjamin Gregorski, Joshua Senecal, Mark Duchaineau, Kenneth I. Joy
    Pages 303-323

  18. Volume Visualization of Multiple Alignment of Large Genomic DNA

    • Nameeta Shah, Scott E. Dillard, Gunther H. Weber, Bernd Hamann
    Pages 325-342

  19. Model-based Visualization - Computing Perceptually Optimal Visualizations

    • Jarke J. van Wijk
    Pages 343-350
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Keywords

About this book

The goal of visualization is the accurate, interactive, and intuitive presentation of data. Complex numerical simulations, high-resolution imaging devices and incre- ingly common environment-embedded sensors are the primary generators of m- sive data sets. Being able to derive scienti?c insight from data increasingly depends on having mathematical and perceptual models to provide the necessary foundation for effective data analysis and comprehension. The peer-reviewed state-of-the-art research papers included in this book focus on continuous data models, such as is common in medical imaging or computational modeling. From the viewpoint of a visualization scientist, we typically collaborate with an application scientist or engineer who needs to visually explore or study an object which is given by a set of sample points, which originally may or may not have been connected by a mesh. At some point, one generally employs low-order piecewise polynomial approximationsof an object, using one or several dependent functions. In order to have an understanding of a higher-dimensional geometrical “object” or function, ef?cient algorithms supporting real-time analysis and manipulation (- tation, zooming) are needed. Often, the data represents 3D or even time-varying 3D phenomena (such as medical data), and the access to different layers (slices) and structures (the underlying topology) comprising such data is needed.

Editors and Affiliations

  • School of Computing Science, Simon Fraser University, Burnaby, Canada

    Torsten Möller, Robert D. Russell

  • Department of Computer Science, University of California, Davis, Davis, USA

    Bernd Hamann

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