In texture segmentation, features must be firstly extracted in the mixture-of-Gaussian (MOG) models. In this paper, we combine MOG model with Gauss Markov random field (GMRF) model and get a unification model. This unified model takes interaction coefficients of neighbor pixels as parameters. We derivate a set of parameters estimation equations by expectation-maximization (EM) algorithms and apply them to a two-class texture segmentation problem. Experimental results show the efficiencies and strengths of the model.


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    Title :

    A unified model of GMRF and MOG for image segmentation


    Contributors:
    Yu Peng, (author) / Tong Xing-Wei, (author) / Feng Ju-Fu, (author)


    Publication date :

    2005-01-01


    Size :

    257434 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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