Title: Image analysis, random fields, and dynamic Monte Carlo methods
First published: 1995
Description: The book is mainly concerned with the mathematical foundations of Bayesian image analysis and its algorithms. This amounts to the study of Markov random fields and dynamic Monte Carlo algorithms like sampling, simulated annealing and stochastic gradient algorithms. The approach is introductory and elementary: given basic concepts from linear algebra and real analysis it is self-contained. No previous knowledge from image analysis is required.

Knowledge of elementary probability theory and statistics is certainly beneficial but not absolutely necessary. The necessary background from imaging is sketched and illustrated by a number of concrete applications like restoration, texture segmentation and motion analysis.
Subjects: Statistical methods, Image processing, Monte Carlo method, Markov random fields, Image analysis
LC classification: TA1637 .W56 1995