圖像分析、隨機(jī)場和動態(tài)蒙特卡羅方法

內(nèi)容概要

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.

書籍目錄

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     1.4 Bayesian Decision Rules   2. Cleaning Dirty Pictures     2.1 Distortion of Images       2.1.1 Physical Digital Imaging Systems       2.1.2 Posterior Distributions     2.2 Smoothing     2.3 Piecewise Smoothing     2.4 Boundary Extraction   3. Random Fields     3.1 Markov Random Fields     3.2 Gibbs Fields and Potentials     3.3 More on Potentials PartⅡ. The Gibbs Sampler and Simulated Annealing   4. Markov Chains: Limit Theorems       4.1 Preliminaries    4.2 The Contraction Coefficient    4.3 Homogeneous Markov Chains    4.4 Inhomogeneous Markov Chains  5.Sampling and Annealing    5.1 Sampling    5.2 Simulated Annealing    5.3 Discussion  6.Cooling Schedules    6.1 The ICM Algorithm    6.2 Exact MAPE Versus Fast Cooling    6.3 Finite Time Annealing  7.Sampling and Annealing Revisited    7.1 A Law of Large Numbers for Inhomogeneous Markov Chains    7.2 A General Theoresm    7.3 Sampling and Annealing Under ConstraintsPartⅢ.More on Sampling and Annealing  8.Metropolis Algorithms  9.Alternative Approaches  10.Parallel AlgorithmsPartⅣ.Texture Analysis  11.Partitioning  12.Texture Models and ClassificationPartⅤ.Parameter Estimation  13.Maximum Likelihood Estimators  14.Spacial ML EstimationPartⅥ.Supplement  15.A Glance at Neural Networks  16.Mixed APplicationsPartⅦ.Appendix  A.Simulation of Random Variables  B.The Perron-Frobenius Theorem  C.Concave Functions  D.A Global Convergence Theorem for Descent AlgorithmsReferencesIndex

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