Low-Rank and Sparse Modeling for Visual Analysis

Yun Fu

ISBN 10: 3319119990 / ISBN 13: 9783319119991

Published by Springer International Publishing Nov 2014, 2014

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 244 pp. Englisch.

Seller Inventory # 9783319119991

Bibliographic Details

• Title: Low-Rank and Sparse Modeling for Visual Analysis
• Publisher: Springer International Publishing Nov 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3319119990
• ISBN 13: 9783319119991
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 18D millimeters 241 millimeters width by 160 millimeters height by 18 millimeters depth
• Weight: 535 grams

About this title

Synopsis

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.

About the Author

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