Advertisement

Message Passing on the Two-Layer Network for Geometric Model Fitting

  • Xing Wang
  • Guobao Xiao
  • Yan Yan
  • Hanzi WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)

Abstract

In this paper, we propose a novel model fitting method to recover multiple geometric structures from data corrupted by noises and outliers. Instead of analyzing each model hypothesis or each data point separately, the proposed method combines both the consensus information in all model hypotheses and the preference information in all data points into a two-layer network, in which the vertices in the first layer represent the data points and the vertices in the second layer represent the model hypotheses. Based on this formulation, the clusters in the second layer of the network, corresponding to the true structures, are detected by using an effective Two-Stage Message Passing (TSMP) algorithm. TSMP can not only accurately detect multiple structures in data without specifying the number of structures, but also handle data even with a large number of outliers. Experimental results on both synthetic data and real images further demonstrate the superiority of the proposed method over several state-of-the-art fitting methods.

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grants U1605252, 61472334 and 61571379.

References

  1. 1.
    Mittal, S., Anand, S., Meer, P.: Generalized projection-based M-estimator. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2351–2364 (2012)CrossRefGoogle Scholar
  2. 2.
    Purkait, P., Chin, T.J., Ackermann, H., Suter, D.: Clustering with hypergraphs: the case for large hyperedges. In: Proceedings of the European Conference on Computer Vision, pp. 672–687 (2014)Google Scholar
  3. 3.
    Magri, L., Fusiello, A.: T-linkage: a continuous relaxation of J-linkage for multi-model fitting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3954–3961 (2014)Google Scholar
  4. 4.
    Raguram, R., Chum, O., Pollefeys, M., Matas, J., Frahm, J.: USAC: a universal framework for random sample consensus. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2022–2038 (2013)CrossRefGoogle Scholar
  5. 5.
    Chin, T.J., Wang, H., Suter, D.: Robust fitting of multiple structures: the statistical learning approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 413–420 (2009)Google Scholar
  6. 6.
    Lazic, N., Givoni, I., Frey, B., Aarabi, P.: FLoSS: facility location for subspace segmentation. In: Proceedings of the IEEE Conference on International Conference on Computer Vision, pp. 825–832 (2009)Google Scholar
  7. 7.
    Wang, H., Chin, T.J., Suter, D.: Simultaneously fitting and segmenting multiple-structure data with outliers. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1177–1192 (2012)CrossRefGoogle Scholar
  8. 8.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Torr, P.H., Zisserman, A.: Mlesac: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78, 138–156 (2000)CrossRefGoogle Scholar
  10. 10.
    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). doi: 10.1007/978-3-540-45243-0_31 CrossRefGoogle Scholar
  11. 11.
    Chum, O., Matas, J.: Matching with PROSAC-progressive sample consensus. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 220–226 (2005)Google Scholar
  12. 12.
    Frahm, J.M., Pollefeys, M.: RANSAC for (quasi-) degenerate data (QDEGSAC). In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 453–460 (2006)Google Scholar
  13. 13.
    Vincent, E., Laganiere, R.: Detecting planar homographies in an image pair. In: Proceedings of the International Symposium on Image and Signal Processing and Analysis, pp. 182–187 (2001)Google Scholar
  14. 14.
    Kanazawa, Y., Kawakami, H.: Detection of planar regions with uncalibrated stereo using distributions of feature points. In: Proceedings of the British Machine Vision Conference, pp. 1–10 (2004)Google Scholar
  15. 15.
    Toldo, R., Fusiello, A.: Robust multiple structures estimation with J-linkage. In: Proceedings of the European Conference on Computer Vision, pp. 537–547 (2008)Google Scholar
  16. 16.
    Wang, H., Xiao, G., Yan, Y., Suter, D.: Mode-seeking on hypergraphs for robust geometric model fitting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2902–2910 (2015)Google Scholar
  17. 17.
    Magri, L., Fusiello, A.: Robust multiple model fitting with preference analysis and low-rank approximation. In: Proceedings of the British Machine Vision Conference, pp. 1–12 (2015)Google Scholar
  18. 18.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Yu, J., Chin, T.J., Suter, D.: A global optimization approach to robust multi-model fitting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2041–2048 (2011)Google Scholar
  20. 20.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Tanimoto, T.T.: Elementary mathematical theory of classification and prediction. Internal IBM Technical Report (1957)Google Scholar
  22. 22.
    Wong, H.S., Chin, T.J., Yu, J., Suter, D.: Dynamic and hierarchical multi-structure geometric model fitting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1044–1051 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and TechnologyXiamen UniversityXiamenChina

Personalised recommendations