Reformulating the Costeira-Kanade algorithm as a pure mathematical theorem independent of the Tomasi-Kanade factorization, we present a robust segmentation algorithm by incorporating such techniques as dimension correction, model selection using the geometric AIC, and least-median fitting. Doing numerical simulations, we demonstrate that oar algorithm dramatically outperforms existing methods. It does not involve any parameters which need to be adjusted empirically.


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    Title :

    Motion segmentation by subspace separation and model selection


    Contributors:


    Publication date :

    2001-01-01


    Size :

    649147 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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