Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
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In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian estimation associated with combinatorial optimization (Simulated Annealing). The segmentation is obtained by classifying the pixels into different pixel classes. These classes are represented by multi-variate Gaussian distributions. Thus, the only hypothesis about the nature of the features is that an additive white noise model is suitable to describe the feature values belonging to a given class. Herein, we use the perceptually uniform CIE-L*u*v* color values as color features and a set of Gabor filters as texture features. We provide experimental results that illustrate the performance of our method on both synthetic and natural color images. Due to the local nature of our MRF model, the algorithm can be highly parallelized.