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Differential methods for multi-dimensional visual data analysis

: Benger, Werner; Heinzl, Rene; Hildenbrand, Dietmar; Weinkauf, Tino; Theisel, Holger; Tschumperlé, David


Scherzer, O.:
Handbook of mathematical methods in imaging. Vol.3
New York: Springer Science+Business Media, 2011 (Springer reference)
ISBN: 978-0-387-92919-4
ISBN: 978-0-387-92921-7
ISBN: 978-0-387-92920-0
Book Article
Fraunhofer IGD ()
geometric algebra (GA); geometric computing; Forschungsgruppe Geometric Algebra Computing (GACO)

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.