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Table of contents (13 papers)
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Front Matter
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Modeling Anisotropy
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Front Matter
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Back Matter
Keywords
About this book
This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain.
The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas.
This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018.
Editors and Affiliations
Bibliographic Information
Book Title: Anisotropy Across Fields and Scales
Editors: Evren Özarslan, Thomas Schultz, Eugene Zhang, Andrea Fuster
Series Title: Mathematics and Visualization
DOI: https://doi.org/10.1007/978-3-030-56215-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2021
Hardcover ISBN: 978-3-030-56214-4Published: 11 February 2021
Softcover ISBN: 978-3-030-56217-5Published: 08 January 2022
eBook ISBN: 978-3-030-56215-1Published: 10 February 2021
Series ISSN: 1612-3786
Series E-ISSN: 2197-666X
Edition Number: 1
Number of Pages: X, 280
Number of Illustrations: 18 b/w illustrations, 91 illustrations in colour
Topics: Visualization, Linear and Multilinear Algebras, Matrix Theory, Computational Science and Engineering, Computer Imaging, Vision, Pattern Recognition and Graphics, Theoretical, Mathematical and Computational Physics