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Measuring the Self-Consistency of Stereo Algorithms

  • Yvan G. Leclerc
  • Q.-Tuan Luong
  • P. Fua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1842)

Abstract

A new approach to characterizing the performance of point-correspondence algorithms is presented. Instead of relying on any “ground truth’, it uses the self-consistency of the outputs of an algorithm independently applied to different sets of views of a static scene. It allows one to evaluate algorithms for a given class of scenes, as well as to estimate the accuracy of every element of the output of the algorithm for a given set of views. Experiments to demonstrate the usefulness of the methodology are presented.

Keywords

Window Size Mahalanobis Distance Scatter Diagram Camera Parameter Stereo Match 
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 2000

Authors and Affiliations

  • Yvan G. Leclerc
    • 1
  • Q.-Tuan Luong
    • 1
  • P. Fua
    • 2
  1. 1.Artificial Intelligence CenterSRI InternationalMenlo Park
  2. 2.LIG, EPFLLausanneSwitzerland

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