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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Hidden multiresolution random fields and their application to image segmentation


    Beteiligte:
    Wilson, R. (Autor:in) / Chang-Tsun Li (Autor:in)


    Erscheinungsdatum :

    01.01.1999


    Format / Umfang :

    259856 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Hidden Multiresolution Random Fields and Their Application to Image Segmentation

    Wilson, R. / Li, C. / IEEE | British Library Conference Proceedings | 1999


    Parameter Estimation in Hidden Fuzzy Markov Random Fields and Image Segmentation

    Salzenstein, F. / Pieczynski, W. | British Library Online Contents | 1997


    A Multiresolution based Image Segmentation

    Kopparapu, S. K. / Mudalige, P. / Corke, P. I. et al. | British Library Conference Proceedings | 1999


    Image Segmentation Based on Multiresolution Filtering

    Zhou, J. / Fang, X. / Ghosh, B. K. et al. | British Library Conference Proceedings | 1994


    Image segmentation based on multiresolution filtering

    Jing Zhou / Xiang Fang / Ghosh, B.J. | IEEE | 1994