A New A Contrario Approach for the Robust Determination of the Fundamental Matrix

  • Ferran Espuny
  • Pascal Monasse
  • Lionel Moisan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8334)

Abstract

The fundamental matrix is a two-view tensor that plays a central role in Computer Vision geometry. We address its robust estimation given correspondences between image features. We use a non-parametric estimate of the distribution of image features, and then follow a probabilistic approach to select the best possible set of inliers among the given feature correspondences. The use of this perception-based a contrario principle allows us to avoid the selection of a precision threshold as in RANSAC, since we provide a decision criterion that integrates all data and method parameters (total number of points, precision threshold, number of inliers given this threshold). Our proposal is analyzed in simulated and real data experiments; it yields a significant improvement of the ORSA method proposed in 2004, in terms of reprojection error and relative motion estimation, especially in situations of low inlier ratios.

Keywords

stereovision fundamental matrix feature matching a contrario model outlier detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ferran Espuny
    • 1
  • Pascal Monasse
    • 2
  • Lionel Moisan
    • 3
  1. 1.School of Environmental SciencesUniversity of LiverpoolUK
  2. 2.LIGM (CNRS UMR 8049), Center for Visual Computing, ENPCUniversité Paris-EstMarne-la-ValléeFrance
  3. 3.MAP5 (CNRS UMR 8145)Université Paris DescartesFrance

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