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 tech

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 Constraints
PartⅢ.More on Sampling and Annealing
8.Metropolis Algorithms
9.Alternative Approaches
10.Parallel Algorithms
PartⅣ.Texture Analysis
11.Partitioning
12.Texture Models and Classification
PartⅤ.Parameter Estimation
13.Maximum Likelihood Estimators
14.Spacial ML Estimation
PartⅥ.Supplement
15.A Glance at Neural Networks
16.Mixed APplications
PartⅦ.Appendix
A.Simulation of Random Variables
B.The Perron-Frobenius Theorem
C.Concave Functions
D.A Global Convergence Theorem for Descent Algorithms
References
Index