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Low-Rank and Sparse Modeling for Visual Analysis
Yun . Ed(S): Fu
€ 127.04
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Description for Low-Rank and Sparse Modeling for Visual Analysis
Paperback. Editor(s): Fu, Yun. Num Pages: 243 pages, 15 black & white illustrations, 51 colour illustrations, 32 black & white tables, biograp. BIC Classification: TTBM; UYQV; UYT. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 13. Weight in Grams: 379.
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.
Product Details
Format
Paperback
Publication date
2016
Publisher
Springer International Publishing AG Switzerland
Number of pages
243
Condition
New
Number of Pages
236
Place of Publication
Cham, Switzerland
ISBN
9783319355672
SKU
V9783319355672
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Yun . Ed(S): Fu
Yun Fu is an Assistant Professor, ECE and CS, Northeastern University
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