| 书目名称 | Information Theory in Computer Vision and Pattern Recognition | | 编辑 | Francisco Escolano,Pablo Suau,Boyán Bonev | | 视频video | | | 概述 | Provides comprehensive coverage of information theory elements implied in modern computer vision and pattern recognition (CVPE) algorithms.Introduces information theory to researchers in CVPR.Addition | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Textbook 2009 | | 关键词 | Computer Vision; Image segmentation; Information; Pattern Recognition; algorithms; entropy; information th | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-84882-297-9 | | isbn_softcover | 978-1-4471-5693-2 | | isbn_ebook | 978-1-84882-297-9 | | copyright | Springer-Verlag London Ltd., part of Springer Nature 2009 |
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发表于 2025-3-21 20:04:46
