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Añadir al carritoTaschenbuch. Condición: Neu. Information Theory in Computer Vision and Pattern Recognition | Francisco Escolano Ruiz (u. a.) | Taschenbuch | xvii | Englisch | 2014 | Springer | EAN 9781447156932 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Information theory has proved to be effective for solving many computer visionand 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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Idioma: Inglés
Publicado por Springer London Nov 2014, 2014
ISBN 10: 1447156935 ISBN 13: 9781447156932
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Information theory has proved to be effective for solving many computer visionand 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. 384 pp. Englisch.
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Añadir al carritoCondición: 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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Idioma: Inglés
Publicado por Springer, Springer Nov 2014, 2014
ISBN 10: 1447156935 ISBN 13: 9781447156932
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 384 pp. Englisch.