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Differential Methods for Multi-Dimensional Visual Data Analysis

Benger, Werner and Heinzl, Rene and Hildenbrand, Dietmar and Weinkauf, Tino and Theisel, Holger and Tschumperlé, David (2011):
Differential Methods for Multi-Dimensional Visual Data Analysis.
Springer Science+Business Media, pp. 1533-1595, DOI: 10.1007/978-0-387-92920-0₃₅,
[Book Section]

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.

Item Type: Book Section
Erschienen: 2011
Creators: Benger, Werner and Heinzl, Rene and Hildenbrand, Dietmar and Weinkauf, Tino and Theisel, Holger and Tschumperlé, David
Title: Differential Methods for Multi-Dimensional Visual Data Analysis
Language: English
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.

Publisher: Springer Science+Business Media
Uncontrolled Keywords: Forschungsgruppe Geometric Algebra Computing (GACO), Geometric algebra (GA), Geometric computing, OpenCL
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-0-387-92920-0₃₅
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