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Sparse Motion Segmentation Using Multiple Six-Point Consistencies

  • Vasileios Zografos
  • Klas Nordberg
  • Liam Ellis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

We present a method for segmenting an arbitrary number of moving objects in image sequences using the geometry of 6 points in 2D to infer motion consistency. The method has been evaluated on the Hopkins 155 database and surpasses current state-of-the-art methods such as SSC, both in terms of overall performance on two and three motions but also in terms of maximum errors. The method works by finding initial clusters in the spatial domain, and then classifying each remaining point as belonging to the cluster that minimizes a motion consistency score. In contrast to most other motion segmentation methods that are based on an affine camera model, the proposed method is fully projective.

Keywords

Image Point Generalise Extreme Value Spectral Cluster Motion Segmentation Epipolar Constraint 
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 2011

Authors and Affiliations

  • Vasileios Zografos
    • 1
  • Klas Nordberg
    • 1
  • Liam Ellis
    • 1
  1. 1.Computer Vision LaboratoryLinköping UniversitySweden

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