出现在书名中的结果
共 0 条
计算机视觉与模式识别中的信息论方法
The book is organized into chapters addressing computer vision and pattern recognition tasks at increasing levels of complexity.The authors have devoted chapters to feature detection and spatial grouping，image segmentation，matching，clustering，feature selection，and classifier design.As the authors address these topics，they gradually introduce techniques from information theory.These include （1） information theoretic measures，such as entropy and Chernoff information，to evaluate image features; (2） mutual informa-tion as a criteria for matching problems (Viola and Wells 1997）; (3） minimal description length ideas (Risannen 1978） and their application to image seg-mentation (Zhu and Yuille 1996）; (4） independent component analysis (Bell and Sejnowski 1995） and its use for feature extraction; (5） the use of rate distortion theory for clustering algorithms; (6） the method of types (Cover and Thomas 1991） and its application to analyze the convergence rates of vision algorithms (Coughlan and Yuille 2002）; and (7） how entropy and infomax principles (Linsker 1988） can be used for classifier design.In addition;the book covers alternative information theory measures，such as Renyi alpha-entropy and Jensen-Shannon divergence，and advanced topics; such as data driven Markov Chain Monte Carlo (Tu and Zhu 2002） and information geo-metry (Amari 1985）.The book describes these theories clearly，giving many illustrations and specifying the code by flowcharts. Overall，the book is a very worthwhile addition to the computer vision and pattern recognition literature.The authors have given an advanced introduction to techniques from probability and information theory and their application to vision and pattern recognition tasks.More importantly，they have described a novel perspective that will be of growing importance over time.As computer vision and pattern recognition develop，the details of these theories will change，but the underlying concepts will remain the same.