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.<>


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    LaValle, S.M. (Autor:in) / Moroney, K.J. (Autor:in) / Hutchinson, S.A. (Autor:in)


    Erscheinungsdatum :

    01.01.1993


    Format / Umfang :

    182819 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch