We describe a method for the temporal tracking of stochastic deformable models in long image sequences. The object representation relies on a hierarchical statistical description of the deformations applied to a template. A Bayesian estimate of the deformations is obtained by maximizing a highly non-linear joint probability distribution. Time consuming global (stochastic) optimization techniques are necessary to obtain optimal solutions unless a good initial guess is available. A good initialization is provided by a recursive temporal filtering of the parameters of the deformable template, combined with a detection of abrupt changes. This procedure yields robust segmentations and enables to track reliably complex deformable structures as is demonstrated here on real-world image sequences showing hand and mouth movements.<>


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    Title :

    Robust tracking of stochastic deformable models in long image sequences


    Contributors:
    Kervrann, C. (author) / Heitz, F. (author)


    Publication date :

    1994-01-01


    Size :

    474368 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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