Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability) [Hardcover]

Gerhard Winkler (Author)

Book Description

ISBN-10: 3540442138 | ISBN-13: 978-3540442134 | Publication Date: February 27, 2006 | Edition: 2nd

"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

Editorial Reviews

Review

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)

About the Author

From the reviews:

"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 doesnât 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, 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)

Product Details

• Hardcover: 360 pages
• Publisher: Springer; 2nd edition (February 27, 2006)
• Language: English
• ISBN-10: 3540442138
• ISBN-13: 978-3540442134
• Product Dimensions: 9.3 x 6.3 x 1.1 inches
• Shipping Weight: 1.6 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,414,654 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

9 of 9 people found the following review helpful:

5.0 out of 5 stars a bible book to learn Gibbs sampler and simulated annealing, July 11, 2000

By A Customer

This review is from: Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction (Applications of Mathematics, 27) (Hardcover)

This is absolute a bible book for any person who want to learn Gibbs sampler and simulated annealing seriously. The format of this book, though full of mathematical equations, is very self-evident and concise. Nothing is missing and nothing is redundent. It is an enjoyable journey to follow the logic and principle in this book, with all your attention in. There are full of in-depth discussion in all aspect of the Gibbs sampler, simulated annealing, from the visiting scheme to cooling schedule, and parallel algorithms. The references are excellent too. The author seems to have read all publications till 1995 about this topic and give an excellent detailed and in-depth survey in his book. At the end of your reading, you would have love the mathematical form the author used. Without these tools, many discussions in this book will be just impossible and groundless. I personally have read this book for several times.