Computer Vision And Shape Recognition

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Information Theory in Computer Vision and Pattern Recognition

Author: Francisco Escolano Ruiz
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-07-14
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.
Computer Vision And Shape Recognition

This is an up-to-date volume of selected and expanded papers originating from Vision Interface 88, a conference held in Edmonton, Canada. A broad range of topics are covered-from image processing to hardware design. They include robot vision, biomedical imaging, remote sensing and parallel processing, shape recognition and features, computational methods in vision, and three dimensional vision and application.
Computer Vision and Shape Recognition

This is an up-to-date volume of selected and expanded papers originating from Vision Interface 88, a conference held in Edmonton, Canada. A broad range of topics are covered-from image processing to hardware design. They include robot vision, biomedical imaging, remote sensing and parallel processing, shape recognition and features, computational methods in vision, and three dimensional vision and application.