In this paper a new class of random field, defined on a multiresolution array structure, is described. Some of the fundamental statistical properties of the model are established. Estimation from noisy data is then considered and a new procedure, multiresolution maximum a posteriori estimation, is defined. These ideas are then applied to the problem of segmenting images containing a number of regions. Implementation of the Bayesian approach is based on a multiresolution form of Gibbs sampling. It is shown that the model forms an excellent basis for the segmentation of such images, which works with no a priori information on the number or sizes of the regions.


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    Title :

    Hidden multiresolution random fields and their application to image segmentation


    Contributors:
    Wilson, R. (author) / Chang-Tsun Li (author)


    Publication date :

    1999-01-01


    Size :

    259856 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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