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Proceedings Paper

Cluster approximations for statistical image processing
Author(s): Chi-hsin Wu; Peter C. Doerschuk
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Paper Abstract

A disadvantage of using discrete-state Markov random field models of images is that optimal estimators for reconstruction problems require excessive and typically random amounts of computation. In one approach the key task is the computation of the conditional mean of the field given the data or equivalently the unconditional mean of the a posteriori field. In this paper we describe a hierarchy of deterministic parallelizable methods for such computations.

Paper Details

Date Published: 29 October 1993
PDF: 8 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162051
Show Author Affiliations
Chi-hsin Wu, Purdue Univ. (Taiwan)
Peter C. Doerschuk, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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