Overview
- Francisco Escolano
Depto. Ciencia de la Computación e Inteligencia Artificial Campus de San Vicente, Universidad Alicante, Alicante, Spain
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- Pablo Suau
Depto. Ciencia de la Computación e Inteligencia Artificial Campus de San Vicente, Universidad Alicante, Alicante, Spain
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- Boyán Bonev
Depto. Ciencia de la Computación e Inteligencia Artificial Campus de San Vicente, Universidad Alicante, Alicante, Spain
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- Provides comprehensive coverage of information theory elements implied in modern computer vision and pattern recognition (CVPE) algorithms
- Introduces information theory to researchers in CVPR
- Additionally, introduces interesting CVPR problems to information theorists
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About this book
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.
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Table of contents (7 chapters)
Front Matter
Pages i-xviiIntroduction
Pages 1-10Interest Points, Edges, and Contour Grouping
Pages 11-41Contour and Region-Based Image Segmentation
Pages 43-104Registration, Matching, and Recognition
Pages 105-156Image and Pattern Clustering
Pages 157-209Feature Selection and Transformation
Pages 211-269Classifier Design
Pages 271-342Back Matter
Pages 343-364
Authors and Affiliations
Depto. Ciencia de la Computación e Inteligencia Artificial Campus de San Vicente, Universidad Alicante, Alicante, Spain
Francisco Escolano, Pablo Suau, Boyán Bonev
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Bibliographic Information
Book Title:Information Theory in Computer Vision and Pattern Recognition
Authors:Francisco Escolano, Pablo Suau, Boyán Bonev
DOI:https://doi.org/10.1007/978-1-84882-297-9
Publisher:Springer London
eBook Packages:Computer Science,Computer Science (R0)
Copyright Information:Springer-Verlag London Ltd., part of Springer Nature 2009
Hardcover ISBN:978-1-84882-296-2Published: 31 July 2009
Softcover ISBN:978-1-4471-5693-2Published: 02 November 2014
eBook ISBN:978-1-84882-297-9Published: 14 July 2009
Edition Number:1
Number of Pages:XVII, 364
Topics:Image Processing and Computer Vision,Pattern Recognition,Probability and Statistics in Computer Science,Artificial Intelligence