In recent years, there have been some attempts to extend one-dimensional hidden Markov model (HMM) to two-dimensions. This paper presents a new statistical model for image modeling and recognition under the assumption that images can be represented by a third-order hidden Markov mesh random field (HMMRF) model. We focus on two major problems: image decoding and parameter estimation. A solution to these problems is derived from the scheme based on a maximum, marginal a posteriori probability criterion for the third-order HMMRF model. We also attempt to illustrate how theoretical results of HMMRF models can be applied to the problems of handwritten character recognition.


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    Titel :

    Hidden Markov mesh random field: theory and its application to handwritten character recognition


    Beteiligte:
    Hee-Seon Park (Autor:in) / Seong-Whan Lee (Autor:in)


    Erscheinungsdatum :

    01.01.1995


    Format / Umfang :

    366086 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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