Sprache: Englisch
Verlag: Springer (India) Private Limited, 2012
ISBN 10: 8132204468 ISBN 13: 9788132204466
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 384 | Sprache: Englisch | Produktart: Bücher | 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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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 384 | Sprache: Englisch | Produktart: Bücher | 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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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 384 | Sprache: Englisch | Produktart: Bücher | 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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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides comprehensive coverage of information theory elements implied in modern computer vision and pattern recognition (CVPE) algorithmsIntroduces information theory to researchers in CVPRAdditionally, introduces interesting CVPR problems.
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In den WarenkorbGebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides comprehensive coverage of information theory elements implied in modern computer vision and pattern recognition (CVPE) algorithmsIntroduces information theory to researchers in CVPRAdditionally, introduces interesting CVPR problems.
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