Image Analysis, Random Fields and Dynamic Monte Carlo Methods A Mathematical Introduction
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 e...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
1995, 1995
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| Edition: | 1st ed. 1995 |
| Series: | Stochastic Modelling and Applied Probability
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| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
| Summary: | 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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| Physical Description: | XIV, 324 p online resource |
| ISBN: | 9783642975226 |