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Compact Fundamental Matrix Computation

  • Kenichi Kanatani
  • Yasuyuki Sugaya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the strict maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably the most practical of all, being small and simple. By numerical experiments, we confirm that our algorithm behaves as expected.

Keywords

Projection Matrix Fundamental Matrix Point Correspondence Computer Vision Application Reprojection Error 
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 2009

Authors and Affiliations

  • Kenichi Kanatani
    • 1
  • Yasuyuki Sugaya
    • 2
  1. 1.Department of Computer ScienceOkayama UniversityOkayamaJapan
  2. 2.Department of Information and Computer SciencesToyohashi University of TechnologyToyohashiJapan

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