InformationTheory in Computer Visionand Pattern Recognition

Escolano, Francisco; Suau, Pablo; Bonev, Boyan

ISBN 10: 1848822960 / ISBN 13: 9781848822962

Edité par Springer, 2009

In. N° de réf. du vendeur ria9781848822962_new

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 across-fertilization of both areas.

Présentation de l'éditeur:

This book provides comprehensive coverage of information theory elements implied in modern CVPR algorithms. It introduces information theory to researchers in CVPR, and additionally introduces interesting CVPR problems to information theorists.

Détails bibliographiques

Titre : InformationTheory in Computer Visionand ...
Éditeur : Springer
Date d'édition : 2009
Reliure : Couverture rigide
Etat : New