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171103 ||| eng |
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|a 9783319613581
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| 100 |
1 |
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|a Schultz, Thomas
|e [editor]
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| 245 |
0 |
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|a Modeling, Analysis, and Visualization of Anisotropy
|h Elektronische Ressource
|c edited by Thomas Schultz, Evren Özarslan, Ingrid Hotz
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| 250 |
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|a 1st ed. 2017
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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| 300 |
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|a X, 407 p. 150 illus. in color
|b online resource
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| 505 |
0 |
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|a Part I: Features and Visualization -- Part II: Image Processing and Analysis -- Part III: Diffusion Modeling and Microstructure -- Part IV: Tractography -- Part V: Machine Learning Approaches
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| 653 |
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|a Computer vision
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| 653 |
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|a Computer Vision
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| 653 |
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|a Mathematics / Data processing
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| 653 |
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|a Information visualization
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| 653 |
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|a Linear Algebra
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| 653 |
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|a Computational Science and Engineering
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| 653 |
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|a Algebras, Linear
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| 653 |
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|a Data and Information Visualization
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| 700 |
1 |
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|a Özarslan, Evren
|e [editor]
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| 700 |
1 |
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|a Hotz, Ingrid
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Mathematics and Visualization
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-61358-1
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-61358-1?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 512.5
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| 520 |
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|a 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 MultivaluedData: 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
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