Locally Optimized RANSAC

  • Ondřej Chum
  • Jiří Matas
  • Josef Kittler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2781)


A new enhancement of ransac, the locally optimized ransac (lo-ransac), is introduced. It has been observed that, to find an optimal solution (with a given probability), the number of samples drawn in ransac is significantly higher than predicted from the mathematical model. This is due to the incorrect assumption, that a model with parameters computed from an outlier-free sample is consistent with all inliers. The assumption rarely holds in practice. The locally optimized ransac makes no new assumptions about the data, on the contrary – it makes the above-mentioned assumption valid by applying local optimization to the solution estimated from the random sample.

The performance of the improved ransac is evaluated in a number of epipolar geometry and homography estimation experiments. Compared with standard ransac, the speed-up achieved is two to three fold and the quality of the solution (measured by the number of inliers) is increased by 10-20%. The number of samples drawn is in good agreement with theoretical predictions.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ondřej Chum
    • 1
  • Jiří Matas
    • 1
    • 2
  • Josef Kittler
    • 2
  1. 1.Faculty of Electrical Engineering, Dept. of CyberneticsCenter for Machine Perception, Czech Technical UniversityPragueCzech Republic
  2. 2.CVSSPUniversity of SurreyGuildfordUnited Kingdom

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