A multistage algorithm which makes use of spatial contextual information in a hierarchical clustering procedure has been developed for unsupervised image segmentation. A Markov random field model is employed to enforce local spatial smoothness, while the maximum entropy principle is utilized to quantify global smoothness in the image processing. A multiwindow approach implemented in a pyramid-like data structure which uses a boundary blocking operation is employed to increase computational efficiency.<>


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    Titel :

    Unsupervised multistage segmentation using Markov random field and maximum entropy principle


    Beteiligte:
    Sanghoon Lee (Autor:in) / Crawford, M.M. (Autor:in)


    Erscheinungsdatum :

    01.01.1994


    Format / Umfang :

    598488 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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