Like New! Image Analysis, Random Fields & Markov Chain Monte Carlo…, Winkler

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Características del artículo

Estado
Como nuevo: Libro en perfecto estado y poco leído. La tapa no tiene desperfectos y si procede, con ...
ISBN
9783540442134
Categoría

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Product Identifiers

Publisher
Springer Berlin / Heidelberg
ISBN-10
3540442138
ISBN-13
9783540442134
eBay Product ID (ePID)
2387055

Product Key Features

Number of Pages
Xvi, 387 Pages
Language
English
Publication Name
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods : a Mathematical Introduction
Subject
Probability & Statistics / Stochastic Processes, Image Processing, Numerical Analysis, Probability & Statistics / General, Computer Vision & Pattern Recognition
Publication Year
2002
Features
Revised
Type
Textbook
Author
Gerhard Winkler
Subject Area
Mathematics, Computers
Series
Stochastic Modelling and Applied Probability Ser.
Format
Hardcover

Dimensions

Item Weight
28.5 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Edition Number
2
Intended Audience
Scholarly & Professional
LCCN
2002-190825
Dewey Edition
21
Reviews
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), 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), 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), 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 fr Mathematik, Vol. 146 (4), 2005), 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. a? 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 a? . he doesna'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 f'r Mathematik, Vol. 146 (4), 2005)
Series Volume Number
27
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
621.36/7/015192
Table Of Content
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.
Edition Description
Revised edition
Synopsis
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 applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required. The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added., "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
LC Classification Number
QA273.A1-274.9

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