An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group's bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that model-guided split and merge yields a significant improvement in segmention over a method that uses merging alone.


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    Titel :

    Region segmentation via deformable model-guided split and merge


    Beteiligte:
    Liu, L. (Autor:in) / Sclaroff, S. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    999050 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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