We propose a variational method for segmenting image sequences into spatiotemporal domains of homogeneous motion. To this end, we formulate the problem of motion estimation in the framework of Bayesian inference, using a prior which favors domain boundaries of minimal surface area. We derive a cost functional which depends on a surface in space-time separating a set of motion regions, as well as a set of vectors modeling the motion in each region. We propose a multiphase level set formulation of this functional, in which the surface and the motion regions are represented implicitly by a vector-valued level set function. Joint minimization of the proposed functional results in an eigenvalue problem for the motion model of each region and in a gradient descent evolution for the separating interface. Numerical results on real-world sequences demonstrate that minimization of a single cost functional generates a segmentation of space-time into multiple motion regions.


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    Title :

    Variational space-time motion segmentation


    Contributors:
    Cremers, (author) / Soatto, (author)


    Publication date :

    2003-01-01


    Size :

    1114894 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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