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Information Theory in Computer Vision and Pattern Recognition

By Francisco Escolano Ruiz , Pablo Suau Pérez , Boyán Ivanov Bonev , Alan L. Yuille

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This book provides comprehensive coverage of information theory elements implied in modern CVPR algorithms. It introduces information theory to researchers in CVPR, and additionally introduces interesting CVPR problems to information theorists.

Full Description

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  • ISBN13: 978-1-8488-2296-2
  • 384 Pages
  • User Level: Science
  • Publication Date: July 14, 2009
  • Available eBook Formats: PDF
Full 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 a cross-fertilization of both areas.
Table of Contents

Table of Contents

  1. Introduction.
  2. Interest Points, Edges and Contour Grouping.
  3. Contour and Region Based Image Segmentation.
  4. Registration, Matching, and Recognition.
  5. Image and Pattern Clustering.
  6. Feature Selection and Transformation.
  7. Classifier Design.

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