Image Analysis, Random Fields and Dynamic Monte Carlo Methods - A Mathematical Introduction (Paperback, Softcover reprint of the original 1st ed. 1995)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Stochastic Modelling and Applied Probability, 27
Release date:	2012
First published:	1995
Authors:	Gerhard Winkler
Dimensions:	235 x 155 x 23mm (L x W x T)
Format:	Paperback
Pages:	324
Edition:	Softcover reprint of the original 1st ed. 1995
ISBN-13:	978-3-642-97524-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > General theory of computing > Mathematical theory of computation Promotions
LSN:	3-642-97524-0
Barcode:	9783642975240

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