Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability) - Softcover
Synopsis
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.
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From the Back Cover
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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Image Analysis, Random Fields and Dynamic Monte Carlo Methods : A Mathematical Introduction
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction (Paperback or Softback)
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods : A Mathematical Introduction
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods : A Mathematical Introduction
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods
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Image Analysis, Random Fields and Dynamic Monte Carlo Methods
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This text is concerned with a probabilistic approach to image analysis as initiated by U. GRENANDER, D. and S. GEMAN, B.R. HUNT and many others, and developed and popularized by D. and S. GEMAN in a paper from 1984. It formally adopts the Bayesian paradigm and therefore is referred to as 'Bayesian Image Analysis'. There has been considerable and still growing interest in prior models and, in particular, in discrete Markov random field methods. Whereas image analysis is replete with ad hoc techniques, Bayesian image analysis provides a general framework encompassing various problems from imaging. Among those are such 'classical' applications like restoration, edge detection, texture discrimination, motion analysis and tomographic reconstruction. The subject is rapidly developing and in the near future is likely to deal with high-level applications like object recognition. Fascinating experiments by Y. CHOW, U. GRENANDER and D.M. KEENAN (1987), (1990) strongly support this belief. 340 pp. Englisch. Seller Inventory # 9783642975240
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