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Image Analysis, Random Fields and Markov Chain Monte Carlo Methods A Mathematical Introduction

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 appli...

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Bibliographic Details
Main Author: Winkler, Gerhard
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2003, 2003
Edition:2nd ed. 2003
Series:Stochastic Modelling and Applied Probability
Subjects:
Numerical Analysis
Computer Vision
Computer Simulation
Statistics 
Radiology
Computer Modelling
Probability Theory
Statistics In Engineering, Physics, Computer Science, Chemistry And Earth Sciences
Probabilities
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • I. Bayesian Image Analysis: Introduction
  • 1. The Bayesian Paradigm
  • 2. Cleaning Dirty Pictures
  • 3. Finite Random Fields
  • II. The Gibbs Sampler and Simulated Annealing
  • 4. Markov Chains: Limit Theorems
  • 5. Gibbsian Sampling and Annealing
  • 6. Cooling Schedules
  • III. Variations of the Gibbs Sampler
  • 7. Gibbsian Sampling and Annealing Revisited
  • 8. Partially Parallel Algorithms
  • 9. Synchronous Algorithms
  • IV. Metropolis Algorithms and Spectral Methods
  • 10. Metropolis Algorithms
  • 11. The Spectral Gap and Convergence of Markov Chains
  • 12. Eigenvalues, Sampling, Variance Reduction
  • 13. Continuous Time Processes
  • V. Texture Analysis
  • 14. Partitioning
  • 15. Random Fields and Texture Models
  • 16. Bayesian Texture Classification
  • VI. Parameter Estimation
  • 17. Maximum Likelihood Estimation
  • 18. Consistency of Spatial ML Estimators
  • 19. Computation of Full ML Estimators
  • VII. Supplement
  • 20. A Glance at Neural Networks
  • 21. Three Applications
  • VIII. Appendix
  • A. Simulation of Random Variables
  • A.1 Pseudorandom Numbers
  • A.2 Discrete Random Variables
  • A.3 Special Distributions
  • B. Analytical Tools
  • B.1 Concave Functions
  • B.2 Convergence of Descent Algorithms
  • B.3 A Discrete Gronwall Lemma
  • B.4 A Gradient System
  • C. Physical Imaging Systems
  • D. The Software Package AntslnFields
  • References
  • Symbols

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