Image Analysis, Random Fields and Dynamic Monte Carlo Methods
Springer Berlin (Verlag)
978-3-642-97524-0 (ISBN)
The text presents Bayesian image analysis and dynamic Monte Carlo algorithms from the mathematical point of view. The subject is introduced at a moderate pace and the proofs are thorough. Specific models are developed step by step and discussed.
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.
Erscheint lt. Verlag | 19.1.2012 |
---|---|
Reihe/Serie | Stochastic Modelling and Applied Probability |
Zusatzinfo | XIV, 324 p. |
Verlagsort | Berlin |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 516 g |
Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
Schlagworte | algorithms • Image Analysis • Imaging • markov random field • Monte Carlo • Monte Carlo Method • Monte Carlos Methods • Probability Theory • Statistics |
ISBN-10 | 3-642-97524-0 / 3642975240 |
ISBN-13 | 978-3-642-97524-0 / 9783642975240 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
aus dem Bereich