Image Processing, Analysis, and Machine Vision 第四版
Image Processing, Analysis, and Machine Vision 第四版
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•A suggestion for partitioning the contents with possible course outlines is included in the books front matter.
•A full set of PowerPoint slides is available for download from this site -- PowerPoints include all images and chapter summaries from the text.
•Each chapter is supported by an extensive list of references and exercises.
•A selection of algorithms is summarized and presented formally in a manner that should aid implementation.
•Reflects the authors' experience in teaching one and two semester undergraduate courses in Digital Image Processing, Digital Image Analysis, Image Understanding, Medical Imaging, Machine Vision, Pattern Recognition, and Intelligent Robotics at their respective institutions.
•Each chapter further includes a concise Summary section.
New to this Edition
•The "Problems and Exercises" part of each chapter has been updated and moved back to the book, rather than being kept in the MATLAB Companion.
•The new edition retains the same Chapter structure, but many sections have been rewritten or introduced as new -- 15% of this new edition consists of newly written material presenting state-of-the-art methods and techniques that have already proven their importance in the field.
•Among the new topics are Radon transform, unified approach to image/template matching, efficient object skeletonization (MB and MB2 algorithms), nearest neighbor classification including BBF/FLANN, random forests, Markov random fields, Gaussian mixture models–expectation maximization, scale invariant feature transform (SIFT), recent 3D image analysis/vision development, texture description using local binary patterns, and several point tracking approaches for motion analysis.
•Chapter 12 has been entirely rewritten.
•Approaches to 3D vision has been heavily revised.
•A full set of PowerPoint slides is available for download from this site -- PowerPoints include all images and chapter summaries from the text.
•Each chapter is supported by an extensive list of references and exercises.
•A selection of algorithms is summarized and presented formally in a manner that should aid implementation.
•Reflects the authors' experience in teaching one and two semester undergraduate courses in Digital Image Processing, Digital Image Analysis, Image Understanding, Medical Imaging, Machine Vision, Pattern Recognition, and Intelligent Robotics at their respective institutions.
•Each chapter further includes a concise Summary section.
New to this Edition
•The "Problems and Exercises" part of each chapter has been updated and moved back to the book, rather than being kept in the MATLAB Companion.
•The new edition retains the same Chapter structure, but many sections have been rewritten or introduced as new -- 15% of this new edition consists of newly written material presenting state-of-the-art methods and techniques that have already proven their importance in the field.
•Among the new topics are Radon transform, unified approach to image/template matching, efficient object skeletonization (MB and MB2 algorithms), nearest neighbor classification including BBF/FLANN, random forests, Markov random fields, Gaussian mixture models–expectation maximization, scale invariant feature transform (SIFT), recent 3D image analysis/vision development, texture description using local binary patterns, and several point tracking approaches for motion analysis.
•Chapter 12 has been entirely rewritten.
•Approaches to 3D vision has been heavily revised.
