This paper introduces the concept of eigen-dynamics and proposes an eigen dynamics analysis (EDA) method to learn the dynamics of natural hand motion from labelled sets of motion captured with a data glove. The result is parameterized with a high-order stochastic linear dynamic system (LDS) consisting of five lower-order LDS. Each corresponding to one eigen-dynamics. Based on the EDA model, we construct a dynamic Bayesian network (DBN) to analyze the generative process of a image sequence of natural hand motion. Using the DBN, a hand tracking system is implemented. Experiments on both synthesized and real-world data demonstrate the robustness and effectiveness of these techniques.


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    Title :

    Tracking articulated hand motion with eigen dynamics analysis


    Contributors:
    Hanning Zhou, (author) / Huang, (author)


    Publication date :

    2003-01-01


    Size :

    898940 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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