Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration [electronic resource] /
Möller, Torsten.editor.
Hamann, Bernd.editor.
Russell, Robert D.editor.
Banff International Research Station for Mathematics Innovation & Discovery.
Visualization is one of the most active and exciting areas of Mathematics and Computing Science, and indeed one which is only beginning to mature. Current visualization algorithms break down for very large data sets. While present approaches use multi-resolution ideas, future data sizes will not be handled that way. New algorithms based on sophisticated mathematical modeling techniques must be devised which will permit the extraction of high-level topological structures that can be visualized. For these reasons a workshop was organized at the Banff International Research Station, focused specifically on mathematical issues. A primary objective of the workshop was to gather together a diverse set of researchers in the mathematical areas relevant to the recent advances in order to discuss the research challenges facing this field in the next several years. The workshop was organized into five different thrusts: - Topology and Discrete Methods - Signal and Geometry Processing - Partial Differential Equations - Data Approximation Techniques - Massive Data Applications This book presents a summary of the research ideas presented at this workshop.
Description based upon print version of record.
Includes bibliographical references.
Maximizing Adaptivity in Hierarchical Topological Models Using Cancellation Trees -- The TOPORRERY: computation and presentation of multi-resolution topology -- Isocontour based Visualization of Time-varying Scalar Fields -- DeBruijn Counting for Visualization Algorithms -- Topological Methods for Visualizing Vortical Flows -- Stability and Computation of Medial Axes - a State-of-the-Art Report -- Local Geodesic Parametrization: an Ant’s Perspective -- Tensor-Fields Visualization Using a Fabric-like Texture Applied to Arbitrary Two-dimensional Surfaces -- Flow Visualization via Partial Differential Equations -- Iterative Twofold Line Integral Convolution for Texture-Based Vector Field Visualization -- Constructing 3D Elliptical Gaussians for Irregular Data -- From Sphere Packing to the Theory of Optimal Lattice Sampling -- Reducing Interpolation Artifacts by Globally Fairing Contours -- Time- and Space-efficient Error Calculation for Multiresolution Direct Volume Rendering -- Massive Data Visualization: A Survey -- Compression and Occlusion Culling for Fast Isosurface Extraction from Massive Datasets -- Volume Visualization of Multiple Alignment of Large Genomic DNA -- Model-based Visualization - Computing Perceptually Optimal Visualizations.
Visualization is one of the most active and exciting areas of Mathematics and Computing Science, and indeed one which is only beginning to mature. Current visualization algorithms break down for very large data sets. While present approaches use multi-resolution ideas, future data sizes will not be handled that way. New algorithms based on sophisticated mathematical modeling techniques must be devised which will permit the extraction of high-level topological structures that can be visualized. For these reasons a workshop was organized at the Banff International Research Station, focused specifically on mathematical issues. A primary objective of the workshop was to gather together a diverse set of researchers in the mathematical areas relevant to the recent advances in order to discuss the research challenges facing this field in the next several years. The workshop was organized into five different thrusts: - Topology and Discrete Methods - Signal and Geometry Processing - Partial Differential Equations - Data Approximation Techniques - Massive Data Applications This book presents a summary of the research ideas presented at this workshop.
Application software.
Computers.
Computer mathematics.
Computer graphics.
Computer science—Mathematics.
Computer Applications.
Theory of Computation.
Computational Mathematics and Numerical Analysis.
Computer Graphics.
Mathematics of Computing.
Computational Science and Engineering.
Mathematics and visualization