1 October 1991 Markov random fields for texture classification
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Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Markov random fields to homogeneous textures are evaluated and compared by visual examination and by standard pattern recognition methodology. The Markov random field model parameters capture the strong cues for human perception, such as directionality, coarseness, and/or contrast. The limited experiments for the classification of natural textures and sandpaper textures by using various classifiers suggest that both feature extraction and classifier design be carefully considered.
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Chaur-Chin Chen, Chaur-Chin Chen, } "Markov random fields for texture classification", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48386; https://doi.org/10.1117/12.48386


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