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Segmentation of Gabor-filtered textures using deterministic relaxation
Research Area: Uncategorized Year: 1996
Type of Publication: Article Keywords: Gaussian distribution, Hopfield neural nets, Markov processes, feature extraction, filtering theory, image representation, image segmentation, image texture, maximum likelihood estimation, probability, random processes, remote sensingGabor filter bank, Ga
Authors: P.P. Raghu, B. Yegnanarayana  
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 frequencies, resolutions, and orientations. The segmentation model consists of feature formation, partition, and competition processes. In the feature formation process, the texture features from the Gabor filter bank are modeled as a Gaussian distribution. The image partition is represented as a noncausal Markov random field (MRF) by means of the partition process. The competition process constrains the overall system to have a single label for each pixel. Using these three random processes, the a posteriori probability of each pixel label is expressed as a Gibbs distribution. The corresponding Gibbs energy function is implemented as a set of constraints on each pixel by using a neural network model based on Hopfield network. A deterministic relaxation strategy is used to evolve the minimum energy state of the network, corresponding to a maximum a posteriori (MAP) probability. This results in an optimal segmentation of the textured image. The performance of the scheme is demonstrated on a variety of images including images from remote sensing
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