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Evaluation and Selection of Models for Motion Segmentation

  • Kenichi Kanatani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)

Abstract

We first present an improvement of the subspace separation for motion segmentation by newly introducing the affine space constraint. We point out that this improvement does not always fare well due to the effective noise it introduces. In order to judge which solution to adopt if different segmentations are obtained, we test two measures using real images: the standard F test, and the geometric model selection criteria.

Keywords

Greedy Algorithm Real Image Space Constraint Space Separation World Coordinate System 
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 2002

Authors and Affiliations

  • Kenichi Kanatani
    • 1
  1. 1.Department of Information TechnologyOkayama UniversityOkayamaJapan

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