Locally Optimized RANSAC

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

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chum, O., Matas, J.: Randomized ransac with T(d,d) test. In: Proceedings of the British Machine Vision Conference, vol. 2, pp. 448–457 (2002)Google Scholar
  2. 2.
    Clarke, J., Carlsson, S., Zisserman, A.: Detecting and tracking linear features efficiently. In: Proc. 7th BMVC, pp. 415–424 (1996)Google Scholar
  3. 3.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. CACM 24(6), 381–395 (1981)MathSciNetGoogle Scholar
  4. 4.
    Hartley, R.: Indefence of the 8-point algorithm. In: ICCV 1995, pp. 1064–1070 (1995)Google Scholar
  5. 5.
    Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Computer Vision and Image Understanding: CVIU 78(1), 99–118 (2000)CrossRefGoogle Scholar
  6. 6.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. of the BMVC, vol. 1, pp. 384–393 (2002)Google Scholar
  7. 7.
    McLauchlan, P., Jaenicke, A.: Image mosaicing using sequential bundle adjustment. In: Proc. BMVC, pp. 616–662 (2000)Google Scholar
  8. 8.
    Myatt, D., Torr, P., Nasuto, S., Bishop, J., Craddock, R.: Napsac: High noise, high dimensional robust estimation - it’s in the bag. In: BMVC 2002, vol. 2, pp. 458–467 (2002)Google Scholar
  9. 9.
    Pritchett, P., Zisserman, A.: Wide baseline stereo matching. In: Proc. International Conference on Computer Vision, pp. 754–760 (1998)Google Scholar
  10. 10.
    Schaffalitzky, F., Zisserman, A.: Viewpoint invariant texture matching and wide baseline stereo. In: Proc. 8th ICCV on Vancouver, Canada (July 2001)Google Scholar
  11. 11.
    Tordoff, B., Murray, D.: Guided sampling and consensus for motion estimation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 82–96. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Torr, P., Zisserman, A., Maybank, S.: Robust detection of degenerate configurations while estimating the fundamental matrix. CVIU 71(3), 312–333 (1998)Google Scholar
  13. 13.
    Torr, P.H.S.: Outlier Detection and Motion Segmentation. PhD thesis, Dept. of Engineering Science, University of Oxford (1995)Google Scholar
  14. 14.
    Torr, P.H.S., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78, 138–156 (2000)CrossRefGoogle Scholar
  15. 15.
    Tuytelaars, T., Van Gool, L.: Wide baseline stereo matching based on local, affinely invariant regions. In: Proc. 11th British Machine Vision Conference (2000)Google Scholar

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

Personalised recommendations