We present an approach to automatically segment and label a continuous observation sequence of hand gestures for a complete unsupervised model acquisition. The method is based on the assumption that gestures can be viewed as repetitive sequences of atomic components, similar to phonemes in speech, governed by a high level structure controlling the temporal sequence. We show that the generating process for the atomic components can be described in gesture space by a mixture of Gaussian, with each mixture component tied to one atomic behaviour. Mixture components are determined using a standard expectation maximisation approach while the determination of the number of components is based on an information criteria, the minimum description length.


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    Titel :

    Auto clustering for unsupervised learning of atomic gesture components using minimum description length


    Beteiligte:
    Walter, M. (Autor:in) / Psarrou, A. (Autor:in) / Gong, S. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    1082792 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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