Low-Rank and Sparse Modeling for Visual Analysis

Unbekannt

ISBN 10: 3319119990 / ISBN 13: 9783319119991

Published by Palgrave Macmillan, 2014

About this Item

Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Seller Inventory # 25013699/1

Bibliographic Details

• Title: Low-Rank and Sparse Modeling for Visual Analysis
• Publisher: Palgrave Macmillan
• Publication Date: 2014
• Language: English
• ISBN 10: 3319119990
• ISBN 13: 9783319119991
• Binding: Hardcover
• Condition: Hervorragend
• Seller catalogs:
  • Bücher

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