Low-Rank and Sparse Modeling for Visual Analysis - Brossura

 
9783319355672: Low-Rank and Sparse Modeling for Visual Analysis

Sinossi

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.

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Informazioni sull?autore

Yun Fu is an Assistant Professor, ECE and CS, Northeastern University

Dalla quarta di copertina

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

· Covers the most state-of-the-art topics of sparse and low-rank modeling

· Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis

· Contributions from top experts voicing their unique perspectives included throughout

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Altre edizioni note dello stesso titolo

9783319119991: Low-Rank and Sparse Modeling for Visual Analysis

Edizione in evidenza

ISBN 10:  3319119990 ISBN 13:  9783319119991
Casa editrice: Springer-Nature New York Inc, 2014
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