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140122 ||| eng |
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|a 9783642975226
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| 100 |
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|a Winkler, Gerhard
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| 245 |
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|a Image Analysis, Random Fields and Dynamic Monte Carlo Methods
|h Elektronische Ressource
|b A Mathematical Introduction
|c by Gerhard Winkler
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| 250 |
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|a 1st ed. 1995
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 1995, 1995
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| 300 |
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|a XIV, 324 p
|b online resource
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|a I. Bayesian Image Analysis: Introduction -- 1. The Bayesian Paradigm -- 2. Cleaning Dirty Pictures -- 3. Random Fields -- II. The Gibbs Sampler and Simulated Annealing -- 4. Markov Chains: Limit Theorems -- 5. Sampling and Annealing -- 6. Cooling Schedules -- 7. Sampling and Annealing Revisited -- III. More on Sampling and Annealing -- 8. Metropolis Algorithms -- 9. Alternative Approaches -- 10. Parallel Algorithms -- IV. Texture Analysis -- 11. Partitioning -- 12. Texture Models and Classification -- V. Parameter Estimation -- 13. Maximum Likelihood Estimators -- 14. Spacial ML Estimation -- VI. Supplement -- 15. A Glance at Neural Networks -- 16. Mixed Applications -- VII. Appendix -- A. Simulation of Random Variables -- B. The Perron-Frobenius Theorem -- C. Concave Functions -- D. A Global Convergence Theorem for Descent Algorithms -- References
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| 653 |
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|a Software engineering
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| 653 |
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|a Computer simulation
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| 653 |
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|a Statistics
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| 653 |
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|a Software Engineering
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| 653 |
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|a Radiology
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| 653 |
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|a Computer Modelling
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| 653 |
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|a Probability Theory
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Probabilities
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| 653 |
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|a Pattern recognition systems
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Stochastic Modelling and Applied Probability
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| 028 |
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|a 10.1007/978-3-642-97522-6
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| 856 |
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|u https://doi.org/10.1007/978-3-642-97522-6?nosfx=y
|x Verlag
|3 Volltext
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|a 519.2
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| 520 |
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|a 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 elemenatry: 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
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