Simultaneous parameter estimation and segmentation of Gibbs randomfields using simulated annealing
Lakshmanan, S.
Derin, H.
Dept. of Electr. & Comput. Eng., Massachusetts Univ., Amherst, MA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 1989
Volume: 11,
Issue: 8
On page(s): 799-813
ISSN: 0162-8828
References Cited: 26
CODEN: ITPIDJ
INSPEC Accession Number: 3477403
Digital Object Identifier: 10.1109/34.31443
Current Version Published: 2002-08-06
Abstract
An adaptive segmentation algorithm is developed which
simultaneously estimates the parameters of the underlying Gibbs random
field (GRF)and segments the noisy image corrupted by additive
independent Gaussian noise. The algorithm, which aims at obtaining the
maximum a posteriori (MAP) segmentation is a simulated annealing
algorithm that is interrupted at regular intervals for estimating the
GRF parameters. Maximum-likelihood (ML) estimates of the parameters
based on the current segmentation are used to obtain the next
segmentation. It is proven that the parameter estimates and the
segmentations converge in distribution to the ML estimate of the
parameters and the MAP segmentation with those parameter estimates,
respectively. Due to computational difficulties, however, only an
approximate version of the algorithm is implemented. The approximate
algorithm is applied on several two- and four-region images with
different noise levels and with first-order and second-order
neighborhoods
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