Synopses & Reviews
"This book is concerned with a probabilistic approach for image analysis, mostly from the Bayesian point of view, and the important Markov chain Monte Carlo methods commonly used....This book will be useful, especially to researchers with a strong background in probability and an interest in image analysis. The author has presented the theory with rigor...he doesn't neglect applications, providing numerous examples of applications to illustrate the theory." -- MATHEMATICAL REVIEWS
Review
From the reviews of the second edition: "This book is concerned with a probabilistic approach for image analysis, mostly from the Bayesian point of view, and the important Markov chain Monte Carlo methods commonly used in this approach. ... this book will be useful, especially to researchers with a strong background in probability and an interest in image analysis. The author has presented the theory with rigor ... . he doesn't neglect applications, providing numerous examples of applications to illustrate the theory and an abundant bibliography pointing to more detailed related work." (Pham Dinh Tuan, Mathematical Reviews, Issue 2004 c) "Based on the Baysian approach the author focuses on the principles of classical image analysis rather than on applications and implementations. Little mathematical knowledge is needed to read the book, thus it is well suited for lectures on image analysis." (Ch. Cenker, Monatshefte für Mathematik, Vol. 146 (4), 2005)
Synopsis
System requirements for accompanying CD-ROM: Windows 95, 98, NT, 2000, XP or ME up to 50 Mbyte free hard disk space.
Includes bibliographical references and index.
Table of Contents
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.