Information theory in computer vision and pattern recognition

Francisco Escolano, Pablo Suau, Boyán Bonev ; foreword by Alan Yuille

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 a cross-fertilization of both areas.

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[目次]

  • Introduction Interest Points, Edges and Contour Grouping Contour and Region Based Image Segmentation Registration, Matching, and Recognition Image and Pattern Clustering Feature Selection and Transformation Classifier Design

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この本の情報

書名 Information theory in computer vision and pattern recognition
著作者等 Escolano, Francisco
Bonev Boyan
Bonev Boyán
Yuille Alan L.
Yuille Alan
Suau Pablo
出版元 Springer
刊行年月 c2009
ページ数 xvii, 355 p., [8] p. of plates
大きさ 25 cm
ISBN 9781848822962
NCID BA91453180
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言語 英語
出版国 オランダ
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