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portada Information Theory in Computer Vision and Pattern Recognition
Type
Physical Book
Author
Preface by
Publisher
Language
English
Pages
364
Format
Paperback
Dimensions
23.4 x 15.6 x 2.0 cm
Weight
0.54 kg.
ISBN13
9781447156932
Categories

Information Theory in Computer Vision and Pattern Recognition

Francisco Escolano Ruiz (Author) · Pablo Suau Pérez (Author) · Alan L. Yuille (Preface by) · Springer · Paperback

Information Theory in Computer Vision and Pattern Recognition - Escolano Ruiz, Francisco ; Yuille, Alan L. ; Suau Pérez, Pablo

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Synopsis "Information Theory in Computer Vision and Pattern Recognition"

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.

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