This text is concerned with a probabilistic approach to image analysis as initiated by U. GRENANDER, D. and S. GEMAN, B.R. HUNT and many others, and developed and popularized by D. and S. GEMAN in a paper from 1984. It formally adopts the Bayesian paradigm and therefore is referred to as "Bayesian Image Analysis".
There has been considerable and still growing interest in prior models and, in particular, in discrete Markov random field methods. Whereas image analysis is replete with ad hoc techniques, Bayesian image analysis provides a general framework encompassing various problems from imaging. Among those are such "classical" applications like restoration, edge detection, texture discrimination, motion analysis and tomographic reconstruction. The subject is rapidly developing and in the near future is likely to deal with high-level applications like object recognition. Fascinating experiments by Y. CHOW,U. GRENANDER and D.M. KEENAN(1987), (1990) strongly support this belief.
本书为英文版。
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数学系写的吧，过于抽象，示例太少，不适合工科学生看
评分数学系写的吧，过于抽象，示例太少，不适合工科学生看
评分数学系写的吧，过于抽象，示例太少，不适合工科学生看
评分数学系写的吧，过于抽象，示例太少，不适合工科学生看
评分数学系写的吧，过于抽象，示例太少，不适合工科学生看
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