We address the problem of articulated posture estimation in its general form. Namely, the recovery of full 3D articulated posture parameters from an uncontrolled scene. Stochastic modeling of low-level segmented image data is unified with models of object kinematic structure through a constrained mixture of observation processes. A modified expectation-maximization algorithm is proposed for this purpose. Early experiments qualitatively demonstrate the efficacy of our approach, and provide a context for integration for more sophisticated image cues.


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    Title :

    Estimation of articulated motion using kinematically constrained mixture densities


    Contributors:
    Hunter, E.A. (author) / Kelly, P.H. (author) / Jain, R.C. (author)


    Publication date :

    1997-01-01


    Size :

    898230 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English