Information Theory In Computer Vision And Pattern Recognition (Sie) (Pb 2012)

Escolano F.

ISBN 10: 8132204468 / ISBN 13: 9788132204466

Published by Springer Exclusive(Cbs), 2012

About this Item

Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide. Seller Inventory # ABNR-67742

Bibliographic Details

Title: Information Theory In Computer Vision And ...

Publisher: Springer Exclusive(Cbs)

Publication Date: 2012

Binding: Soft cover

Condition: New

Edition: International Edition

About this title

Synopsis:

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.