Near Real-Time Motion Segmentation Using Graph Cuts

  • Thomas Schoenemann
  • Daniel Cremers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


We present a new approach to integrated motion estimation and segmentation by combining methods from discrete and continuous optimization. The velocity of each of a set of regions is modeled as a Gaussian-distributed random variable and motion models and segmentation are obtained by alternated maximization of a Bayesian a-posteriori probability. We show that for fixed segmentation the model parameters are given by a closed-form solution. Given the velocities, the segmentation is in turn determined using graph cuts which allows a globally optimal solution in the case of two regions. Consequently, there is no contour evolution based on differential increments as for example in level set methods. Experimental results on synthetic and real data show that good segmentations are obtained at speeds close to real-time.


Computer Vision Motion Estimation Motion Segmentation Optimal Segmentation Segmentation Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thomas Schoenemann
    • 1
  • Daniel Cremers
    • 1
  1. 1.CVPR GroupUniversity of BonnBonnGermany

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