Information Theory in Computer Vision and Pattern Recognition

July 31, 2009|
Information Theory in Computer Vision and Pattern Recognition by Francisco Escolano Ruiz
$168.95
Hardcover
Earn 845 plum® points
Buy Online
Ship to an address
Free shipping on orders over $35
Pick up in store
To see if pickup is available,
Find In Store
Not sold in stores
Prices and offers may vary in store

about

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.

Title:Information Theory in Computer Vision and Pattern Recognition
Format:Hardcover
Product dimensions:364 pages, 9.25 X 6.1 X 0 in
Shipping dimensions:364 pages, 9.25 X 6.1 X 0 in
Published:July 31, 2009
Publisher:Springer-Verlag/Sci-Tech/Trade
Language:English
Appropriate for ages:All ages
ISBN - 13:9781848822962

Recently Viewed
|