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Differential Methods for Multi-Dimensional Visual Data Analysis
Max Planck Institute for Informatics, Germany.ORCID iD: 0000-0002-1498-9062
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2010 (English)In: Handbook of Mathematical Methods in Imaging / [ed] Scherzer, O., Springer , 2010, 1533-1595 p.Chapter in book (Other academic)Text
Abstract [en]

Images in scientific visualization are the end-product of the 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.

Place, publisher, year, edition, pages
Springer , 2010. 1533-1595 p.
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
URN: urn:nbn:se:kth:diva-184773DOI: 10.1007/978-1-4939-0790-8_35ISBN: 978-0-387-92921-7OAI: diva2:916784

QC 20160418

Available from: 2016-04-04 Created: 2016-04-04 Last updated: 2016-04-18Bibliographically approved

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Weinkauf, Tino
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ReferencesLink to record
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