Efficient Multi-structure Robust Fitting with Incremental Top-k Lists Comparison

  • Hoi Sim Wong
  • Tat-Jun Chin
  • Jin Yu
  • David Suter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6495)

Abstract

Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-k lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures can be quickly retrieved and (2) filters the hypotheses such that only a small but very promising subset remain. This permits the usage of simple agglomerative clustering on the surviving hypotheses for accurate model selection. The outcome is a highly efficient multi-structure robust estimation technique. Experiments on synthetic and real data show the superior performance of our approach over previous methods.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)CrossRefMATHGoogle Scholar
  2. 2.
    Fischler, M.A., Bolles, R.C.: RANSAC: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  4. 4.
    Tordoff, B.J., Murray, D.W.: Guided-MLESAC: Faster image transform estimation by using matching priors. TPAMI 27, 1523–1535 (2005)CrossRefGoogle Scholar
  5. 5.
    Chum, O., Matas, J.: Matching with PROSAC- progressive sample consensus. In: CVPR (2005)Google Scholar
  6. 6.
    Sattler, T., Leibe, B., Kobbelt, L.: SCRAMSAC: Improving RANSAC’s efficiency with a spatial consistency filter. In: ICCV (2009)Google Scholar
  7. 7.
    Stewart, C.V.: Robust parameter estimation in Computer Vision. SIAM Review 41, 513–537 (1999)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Vincent, E., Laganiere, R.: Detecting planar homographies in an image pair. In: Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, ISPA 2001, pp. 182–187 (2001)Google Scholar
  9. 9.
    Kanazawa, Y., Kawakami, H.: Detection of planar regions with uncalibrated stereo using distributions of feature points. In: BMVC (2004)Google Scholar
  10. 10.
    Zuliani, M., Kenney, C., Manjunath, B.: The multiransac algorithm and its application to detect planar homographies. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 3 (2005)Google Scholar
  11. 11.
    Toldo, R., Fusiello, A.: Robust multiple structures estimation with j-linkage. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 537–547. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Xu, L., Oja, E., Kultanen, P.: A new curve detection method: randomized Hough transform (RHT). Pattern Recognition Letters 11, 331–338 (1990)CrossRefMATHGoogle Scholar
  13. 13.
    Fagin, R., Kumar, R., Sivakumar, D.: Comparing Top shapek Lists. In: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, vol. 36. SIAM, Philadelphia (2003)Google Scholar
  14. 14.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2009)CrossRefMATHGoogle Scholar
  15. 15.
    Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hoi Sim Wong
    • 1
  • Tat-Jun Chin
    • 1
  • Jin Yu
    • 1
  • David Suter
    • 1
  1. 1.School of Computer ScienceThe University of AdelaideAustralia

Personalised recommendations