Combining Geometric and Appearance Priors for Robust Homography Estimation

  • Eduard Serradell
  • Mustafa Özuysal
  • Vincent Lepetit
  • Pascal Fua
  • Francesc Moreno-Noguer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)


The homography between pairs of images are typically computed from the correspondence of keypoints, which are established by using image descriptors. When these descriptors are not reliable, either because of repetitive patterns or large amounts of clutter, additional priors need to be considered. The Blind PnP algorithm makes use of geometric priors to guide the search for matches while computing camera pose. Inspired by this, we propose a novel approach for homography estimation that combines geometric priors with appearance priors of ambiguous descriptors. More specifically, for each point we retain its best candidates according to appearance. We then prune the set of potential matches by iteratively shrinking the regions of the image that are consistent with the geometric prior. We can then successfully compute homographies between pairs of images containing highly repetitive patterns and even under oblique viewing conditions.


Gaussian Mixture Model Model Point Candidate Selection Repetitive Pattern Potential 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 2010

Authors and Affiliations

  • Eduard Serradell
    • 1
  • Mustafa Özuysal
    • 2
  • Vincent Lepetit
    • 2
  • Pascal Fua
    • 2
  • Francesc Moreno-Noguer
    • 1
  1. 1.Institut de Robòtica i Informàtica IndustrialCSIC-UPCBarcelonaSpain
  2. 2.Computer Vision LaboratoryEPFLLausanneSwitzerland

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