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Springer Information Theory in Computer Vision and Pattern Recognition

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£77.59 £76.30 £76.58 £76.86 £77.15 £77.43 £77.71 26 July 2026 10 August 2026 25 August 2026 09 September 2026 24 September 2026
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8 black & white tables, biography

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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 Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 July 2009
Listed Since
22 October 2008

Barcode

No barcode data available

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