Information Theory in Computer Vision and Pattern Recognition

Information Theory in Computer Vision and Pattern Recognition by Francisco Escolano Ruiz, published by Springer London on July 31, 2009, spans 364 pages and is presented in English. This book delves into the application of information theory to various computer vision and pattern recognition challenges, including image matching, clustering, and feature selection. It introduces key concepts such as entropy and mutual information, while also discussing principles like maximum entropy and rate distortion theory, providing a structured approach to understanding these elements within the context of CVPR.

Readers will find a comprehensive exploration of how information theory can address specific CVPR problems, with an emphasis on formulating these challenges and presenting representative algorithms. The book highlights the connections between information theory principles and their applications, aiming to create a research roadmap that benefits both CVPR and machine learning researchers. This edition serves as a valuable resource for those interested in the intersection of these fields, offering insights into mathematical and statistical software relevant to computer graphics and optical data processing.