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＜電子ブック＞
Topological Data Analysis for Scientific Visualization / by Julien Tierny
(Mathematics and Visualization. ISSN:2197666X)

版	1st ed. 2017.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2017
本文言語	英語
大きさ	XV, 150 p. 84 illus. in color : online resource
著者標目	*Tierny, Julien author SpringerLink (Online service)
件　名	LCSH:Information visualization LCSH:Topology LCSH:Image processing -- Digital techniques  全ての件名で検索 LCSH:Computer vision LCSH:Algorithms LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Discrete mathematics LCSH:Computer software FREE:Data and Information Visualization FREE:Topology FREE:Computer Imaging, Vision, Pattern Recognition and Graphics FREE:Algorithms FREE:Discrete Mathematics in Computer Science FREE:Mathematical Software
一般注記	1. Introduction -- 2. Background: 2.1 Data representation -- 2.2 Topological abstractions -- 2.3 Algorithms and applications -- 3. Abstraction: 3.1 Efficient topological simplification of scalar fields -- 3.2 Efficient Reeb graph computation for volumetric meshes -- 4. Interaction: 4.1 Topological simplification of isosurfaces -- 4.2 Interactive editing of topological abstractions -- 5. Analysis: 5.1 Exploration of turbulent combustion simulations -- 5.2 Quantitative analysis of molecular interactions -- 6. Perspectives: 6.1 Emerging constraints -- 6.2 Emerging data types -- 7. Conclusion Combining theoretical and practical aspects of topology, this book delivers a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a thorough but intuitive manner, with many high-quality color illustrations. Key algorithms for the computation and simplification of topological data representations are described in details, and their application is carefully illustrated in a chapter dedicated to concrete use cases. With its fine balance between theory and practice, "Topological Data Analysis for Scientific Visualization" constitutes an appealing introduction to the increasingly important topic of topological data analysis, for lecturers, students and researchers HTTP:URL=https://doi.org/10.1007/978-3-319-71507-0

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