Clustering methods, which are frequently employed for region-based segmentation, are inherently metric based. A fundamental problem with an estimation-based criterion is that as the amount of information in a region decreases, the parameter estimates become extremely unreliable and incorrect decisions are likely to be made. It is shown that clustering need not be metric based. A rigorous region merging probability function is used. It makes use of all information available in the probability densities of a statistical image model. By using this probability function as a termination criterion it is possible to produce segmentations in which all region merges are performed above some level of confidence.<>


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    Title :

    Agglomerative clustering on range data with a unified probabilistic merging function and termination criterion


    Contributors:


    Publication date :

    1993-01-01


    Size :

    182819 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English