Simultaneous optimal segmentation and model estimation ofnonstationary noisy images
Goutsias, J.
Mendel, J.M.
Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sep 1989
Volume: 11,
Issue: 9
On page(s): 990-998
ISSN: 0162-8828
References Cited: 20
CODEN: ITPIDJ
INSPEC Accession Number: 3509346
Digital Object Identifier: 10.1109/34.35503
Current Version Published: 2002-08-06
Abstract
The authors present the class of semi-Markov random fields and
deal, in particular, with the subclass of discrete-valued, nonsymmetric
half-plane, unilateral Markov random fields. A hierarchical
nonstationary-mean nonstationary-variance (NMNV) image model is proposed
for the modeling of nonstationary and noisy images. This model seems to
be advantageous as compared to a regular NMNV model because it
statistically incorporates the correlation between pixels around the
boundary of two adjacent regions. The hierarchical NMNV model leads to
the development of an optimal algorithm that allows the simultaneous
segmentation and model estimation of measured images. Although no
theoretical result is available for the consistency of the estimated
model parameters, the method seems to work sufficiently well for the
examples considered
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