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    Anisotropy Across Fields and Scales

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    Contributor(s)
    Özarslan, Evren (editor)
    Schultz, Thomas (editor)
    Zhang, Eugene (editor)
    Fuster, Andrea (editor)
    Language
    English
    Show full item record
    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/46809
    Keywords
    Visualization; Linear and Multilinear Algebras, Matrix Theory; Computational Science and Engineering; Computer Imaging, Vision, Pattern Recognition and Graphics; Theoretical, Mathematical and Computational Physics; Data and Information Visualization; Linear Algebra; tensor; tensor fields; higher-order harmonics; spherical harmonics; image processing; medical imaging; diffusion-weighted imaging (DWI); structural mechanics; astrophysics; statistics; open access; Combinatorics & graph theory; Algebra; Maths for scientists; Computer vision; Mathematical physics
    DOI
    10.1007/978-3-030-56215-1
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    2021
    Imprint
    Springer
    Series
    Mathematics and Visualization,
    Classification
    Combinatorics and graph theory
    Algebra
    Maths for scientists
    Computer vision
    Mathematical physics
    Pages
    280
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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