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Information Theory in Computer Vision and Pattern Recognition

By (author) Francisco Escolano Ruiz, Foreword by Alan L. Yuille, By (author) Pablo Suau Pérez, By (author) Boyán Ivanov Bonev

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normal price: R 3,722.95

Price: R 3,350.95

| book description |

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.

| product details |



Normally shipped | Available from overseas. Delivery time is 14 - 21 days
Publisher | Springer London Ltd
Published date | 2 Nov 2014
Language |
Format | Paperback / softback
Pages | 364
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-1-4471-5693-2
Readership Age |
BISAC | computers / computer vision

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