Information Theory in Computer Vision and Pattern Recognition (Paperback, 2009 ed.)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Release date:	November 2014
First published:	2009
Foreword by:	Alan L. Yuille
Authors:	Francisco Escolano Ruiz • Pablo Suau Perez • Boyan Ivanov Bonev
Dimensions:	235 x 155 x 20mm (L x W x T)
Format:	Paperback
Pages:	364
Edition:	2009 ed.
ISBN-13:	978-1-4471-5693-2
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Computing & IT > Applications of computing > Pattern recognition Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Computing & IT > Applications of computing > Image processing > General
LSN:	1-4471-5693-5
Barcode:	9781447156932

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