Hidden multiresolution random fields and their application to image segmentation

Roland Wilson, Chang Tsun Li

    Research output: Book chapter/Published conference paperConference paperpeer-review

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings - International Conference on Image Analysis and Processing, ICIAP 1999
    Pages346-351
    Number of pages6
    DOIs
    Publication statusPublished - 1999
    Event10th International Conference on Image Analysis and Processing, ICIAP 1999 - Venice, Italy
    Duration: 27 Sep 199929 Sep 1999

    Conference

    Conference10th International Conference on Image Analysis and Processing, ICIAP 1999
    Country/TerritoryItaly
    CityVenice
    Period27/09/9929/09/99

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