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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    Title :

    Unsupervised multistage segmentation using Markov random field and maximum entropy principle


    Contributors:


    Publication date :

    1994-01-01


    Size :

    598488 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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