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Near Real-Time Motion Segmentation Using Graph Cuts

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Pattern Recognition (DAGM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4174))

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Abstract

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.

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Author information

Authors and Affiliations

  1. CVPR Group, University of Bonn, Römerstr. 164, 53117, Bonn, Germany

    Thomas Schoenemann & Daniel Cremers

Authors
  1. Thomas Schoenemann
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  2. Daniel Cremers
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Editor information

Editors and Affiliations

  1. Norwegian Information Security Laboratory, Gjøvik University College, Norway

    Katrin Franke

  2. Fraunhofer FIRST (IDA), Berlin, Germany

    Klaus-Robert Müller

  3. Department of Security Technology, Fraunhofer Institute for Production Systems and Design Technology (IPK), Pascalstr. 8-9, 10587, Berlin, Germany

    Bertram Nickolay

  4. Department of Electronic Imaging Technology, Fraunhofer Institute for Information and Communication Technology, Heinrich Hertz Institute (HHI), Einsteinufer 37, 10587, Berlin, Germany

    Ralf Schäfer

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© 2006 Springer-Verlag Berlin Heidelberg

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Schoenemann, T., Cremers, D. (2006). Near Real-Time Motion Segmentation Using Graph Cuts. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_46

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  • DOI: https://doi.org/10.1007/11861898_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44412-1

  • Online ISBN: 978-3-540-44414-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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