Pp. Raghu et B. Yegnanarayana, SEGMENTATION OF GABOR-FILTERED TEXTURES USING DETERMINISTIC RELAXATION, IEEE transactions on image processing, 5(12), 1996, pp. 1625-1636
A supervised texture segmentation scheme is proposed in this article.
The texture features are extracted by filtering the given image using
a filter bank consisting of a number of Gabor filters with different f
requencies, resolutions, and orientations, The segmentation model cons
ists of feature formation, partition, and competition processes, In th
e feature formation process, the texture features from the Gabor filte
r bank are modeled as a Gaussian distribution, The image partition is
represented as a noncausal Markov random field (MRF) by means of the p
artition process, The competition process constrains the overall syste
m to have a single label for each pixel, Using these three random proc
esses, the a posteriori probability of each pixel label is expressed a
s a Gibbs distribution, The corresponding Gibbs energy function is imp
lemented as a set of constraints on each pixel by using a neural netwo
rk model based on Hopfield network, A deterministic relaxation strateg
y is used to evolve the minimum energy state of the network, correspon
ding to a maximum a posteriori (MAP) probability, This results in an o
ptimal segmentation of the textured image. The performance of the sche
me is demonstrated on a variety of images including images from remote
sensing.