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Modeling, Analysis, and Visualization of Anisotropy
Institute of Computer Science, University of Bonn, Bonn, Germany.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7285-0483
2017 (English)Collection (editor) (Refereed)
Abstract [en]

This book focuses on the modeling, processing and visualization of anisotropy, irrespective of the context in which it emerges, using state-of-the-art mathematical tools. As such, it differs substantially from conventional reference works, which are centered on a particular application. It covers the following topics: (i) the geometric structure of tensors, (ii) statistical methods for tensor field processing, (iii) challenges in mapping neural connectivity and structural mechanics, (iv) processing of uncertainty, and (v) visualizing higher-order representations. In addition to original research contributions, it provides insightful reviews.This multidisciplinary book is the sixth in a series that aims to foster scientific exchange between communities employing tensors and other higher-order representations of directionally dependent data. A significant number of the chapters were co-authored by the participants of the workshop titled Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis, which was held in Dagstuhl, Germany in April 2016.

It offers a valuable resource for those working in the field of multi-directional data, vital inspirations for the development of new models, and essential analysis and visualization techniques, thus furthering the state-of-the-art in studies involving anisotropy.

Place, publisher, year, edition, pages
Cham: Springer, 2017. , p. 407
Series
Mathematics and Visualization, ISSN 1612-3786, E-ISSN 2197-666X ; 2017
National Category
Telecommunications Social Sciences Interdisciplinary
Identifiers
URN: urn:nbn:se:liu:diva-152348DOI: 10.1007/978-3-319-61358-1Libris ID: p71gtmr118cqdbqISBN: 9783319613574 (print)ISBN: 9783319613581 (electronic)OAI: oai:DiVA.org:liu-152348DiVA, id: diva2:1259338
Available from: 2018-10-29 Created: 2018-10-29 Last updated: 2019-09-12Bibliographically approved

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Özarslan, EvrenHotz, Ingrid

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CiteExportLink to record
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Citation style
  • apa
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