Differential Methods for Multi-Dimensional Visual Data Analysis

Werner Benger, René Heinzl, Dietmar Hildenbrand, Tino Weinkauf, Holger Theisel and David Tschumperlé
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Werner Benger: Center for Computation and Technology at Lousiana State University
René Heinzl: Shenteq s.r.o
Dietmar Hildenbrand: University of Technology Darmstadt
Tino Weinkauf: New York University
Holger Theisel: Institut fur Simulation und Graphik AG Visual Computing
David Tschumperlé: GREYC (UMR-CNRS 6072)

Chapter 35 in Handbook of Mathematical Methods in Imaging, 2011, pp 1533-1595 from Springer

Abstract: Abstract Images in scientific visualization are the end-product of data processing. Starting from higher-dimensional datasets, such as scalar-, vector-, tensor- fields given on 2D, 3D, 4D domains, the objective is to reduce this complexity to two-dimensional images comprehensible to the human visual system. Various mathematical fields such as in particular differential geometry, topology (theory of discretized manifolds), differential topology, linear algebra, Geometric Algebra, vectorfield and tensor analysis, and partial differential equations contribute to the data filtering and transformation algorithms used in scientific visualization. The application of differential methods is core to all these fields. The following chapter will provide examples from current research on the application of these mathematical domains to scientific visualization and ultimately generating of images for analysis of multi-dimensional datasets.

Keywords: Vector Field; Fiber Bundle; Base Space; Tensor Field; Geometric Algebra (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/978-0-387-92920-0_35

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