Information Theory in Computer Vision and Pattern Recognition is popular PDF and ePub book, written by Francisco Escolano Ruiz in 2009-07-14, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Information Theory in Computer Vision and Pattern Recognition can be Read Online from any device for your convenience.
Information Theory in Computer Vision and Pattern Recognition Book PDF Summary
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.
Detail Book of Information Theory in Computer Vision and Pattern Recognition PDF
- Author : Francisco Escolano Ruiz
- Release : 14 July 2009
- Publisher : Springer Science & Business Media
- ISBN : 9781848822979
- Genre : Computers
- Total Page : 375 pages
- Language : English
- PDF File Size : 19,9 Mb
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