IMAGE ANALYSIS, RANDOM FIELDS AND MARKOV CHAIN MONTE CARLO METHODS

This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required.The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added. This second edition comes with a CD-ROM by F. Friedrich,containing a host of (live) illustrations for each chapter. In an interactive environment, readers can perform their own experiments to consolidate the subject. TOC:I.Bayesian Image Analysis.- Introduction.- The Bayesian Paradigm.- Cleaning Dirty Pictures.- Random Fields.- II. The Gibbs Sampler and Simulated Annealing.- Markov Chains: Limit Theorems.- Gibbsian Sampling and Annealing.- Cooling Schedules.- Gibbsian Sampling and Annealing Revisited.- III. More on Sampling and Annealing.- Metropolis Algorithms.- Eigenvalues and Related Topics.- Parallel Algorithms.- IV. Texture Analysis.- Partitioning.- Random Fields and Texture Models.- Bayesian Texture Classification.- V. Parameter Estimation.- Maximum Likelihood Estimation.- Consistency of Spatial ML .- Computation of full ML Estimators.- VI. Supplement.- A Glance at Neural Networks.- Three Applications.- VII. Appendix.- A Simulation of Random Variables.-B Analytical Tools.- C Physical Imaging Systems.