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Efficient and Scalable 4th-Order Match Propagation

  • David Ok
  • Renaud Marlet
  • Jean-Yves Audibert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)

Abstract

We propose a robust method to match image feature points taking into account geometric consistency. It is a careful adaptation of the match propagation principle to 4th-order geometric constraints (match quadruple consistency). With our method, a set of matches is explained by a network of locally-similar affinities. This approach is useful when simple descriptor-based matching strategies fail, in particular for highly ambiguous data, e.g., with repetitive patterns or where texture is lacking. As it scales easily to hundreds of thousands of matches, it is also useful when denser point distributions are sought, e.g., for high-precision rigid model estimation. Experiments show that our method is competitive (efficient, scalable, accurate, robust) against state-of-the-art methods in deformable object matching, camera calibration and pattern detection.

Keywords

Interest Point Camera Calibration Pattern Detection Repetitive Pattern Sift Descriptor 
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 2013

Authors and Affiliations

  • David Ok
    • 1
  • Renaud Marlet
    • 1
  • Jean-Yves Audibert
    • 1
  1. 1.Center for Visual Computing, École des Ponts ParisTechUniversité Paris-Est, LIGM (UMR CNRS)Marne-la-ValléeFrance

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