Based on a geometric interpretation of the optic flow constraint equation, we propose a conditional probability on the spatio-temporal image gradient. We consistently derive a variational approach for the segmentation of the image domain into regions of homogeneous motion. The proposed energy functional extends the Mumford-Shah functional from gray value segmentation to motion segmentation. It depends on the spatio-temporal image gradient calculated from only two consecutive images of an image sequence. Moreover, it depends on motion vectors for a set of regions and a boundary separating these regions. In contrast to most alternative approaches, the problems of motion estimation and motion segmentation are jointly solved by minimizing a single functional. Numerical evaluation with both explicit and implicit (level set based) representations of the boundary shows the strengths and limitations of our approach.


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    Title :

    A variational framework for image segmentation combining motion estimation and shape regularization


    Contributors:
    Cremers, D. (author)


    Publication date :

    2003-01-01


    Size :

    810339 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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    Statistical shape knowledge in variational motion segmentation

    Cremers, D. / Schnorr, C. | British Library Online Contents | 2003