Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction

Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction

by Gerhard Winkler
     
 

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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

Overview

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.

Product Details

ISBN-13:
9783642975240
Publisher:
Springer Berlin Heidelberg
Publication date:
01/19/2012
Series:
Stochastic Modelling and Applied Probability Series , #27
Edition description:
Softcover reprint of the original 1st ed. 1995
Pages:
324
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

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