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.
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.
€106.95
表中的内容
Introduction Interest Points, Edges and Contour Grouping Contour and Region Based Image Segmentation Registration, Matching, and Recognition Image and Pattern Clustering Feature Selection and Transformation Classifier Design
语言 英语 ● 格式 PDF ● 网页 364 ● ISBN 9781848822979 ● 文件大小 20.1 MB ● 出版者 Springer London ● 市 London ● 国家 GB ● 发布时间 2009 ● 下载 24 个月 ● 货币 EUR ● ID 2151895 ● 复制保护 Adobe DRM
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