品牌：
基本信息
·出版社：世界图书出版公司
·页码：324 页码
·出版日：1999年
·ISBN：750623825X
·条码：9787506238250
·版次：1999年3月第1版
·装帧：平装
·开本：24开 24开
内容简介
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。
编辑推荐
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.
目录
Introduction
PartⅠ. Bayesian Image Analysis: Introduction
1. The Bayesian Paradigm
1.1 The Space of Images
1.2 The Space of Observations
1.3 Prior and Posterior Distribution
……[看更多目录]
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