Parameter estimation is a key computational issue in all statistical image modeling techniques. In this paper, we explore a computationally efficient parameter estimation algorithm for multi-dimensional hidden Markov models. 2-D HMM has been applied to supervised aerial image classification and comparisons have been made with the first proposed estimation algorithm. An extensive parametric study has been performed with 3-D HMM and the scalability of the estimation algorithm has been discussed. Results show the great applicability of the explored algorithm to multi-dimensional HMM based image modeling applications.


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    Titel :

    Parameter estimation of multi-dimensional hidden Markov models - a scalable approach


    Beteiligte:
    Joshi, D. (Autor:in) / Jia Li, (Autor:in) / Wang, J.Z. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    271343 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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