Better Correspondence by Registration

  • Shufei Fan
  • Rupert Brooks
  • Frank P. Ferrie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)


Accurate image correspondence is crucial for estimating multiple-view geometry. In this paper, we present a registration-based method for improving accuracy of the image correspondences. We apply the method to fundamental matrix estimation under practical situations where there are both erroneous matches (outliers) and small feature location errors. Our registration-based method can correct feature locational error to less than 0.1 pixel, remedying localization inaccuracy due to feature detectors. Moreover, we carefully examine feature similarity based on their post-alignment appearance, providing a more reasonable prior for subsequent outlier detection. Experiments show that we can improve feature localization accuracy of the MSER feature detector, which recovers the most accurate feature localization as reported in a recent study by Haja and others. As a result of applying our method, we recover the fundamental matrix with better accuracy and more efficiency.


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  1. 1.
    Torr, P., Murray, D.: The development and comparison of robust methods for estimating the fundamental matrix. International Journal of Computer Vision 24(3), 271–300 (1997)CrossRefGoogle Scholar
  2. 2.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Torr, P., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78(1), 138–156 (2000)CrossRefGoogle Scholar
  4. 4.
    Tordoff, B., Murray, D.: Guided-MLESAC: Faster image transform estimation by using matching priors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1523–1535 (2005)CrossRefGoogle Scholar
  5. 5.
    Chum, O., Matas, J.: Matching with PROSAC: Progressive sample consensus. In: Proceedings of Computer Vision and Pattern Recognition, pp. I: 220–226 (2005)Google Scholar
  6. 6.
    Haja, A., Jahne, B., Abraham, S.: Localization accuracy of region detectors. In: Proceedings of Computer Vision and Pattern Recognition, June 2008, pp. 1–8 (2008)Google Scholar
  7. 7.
    Kanatani, K., Sugaya, Y.: High accuracy fundamental matrix computation and its performance evaluation. IEICE Transactions on Information and Systems E90-D(2), 579–585 (2007)CrossRefGoogle Scholar
  8. 8.
    Chojnacki, W., Brooks, M., van den Hengel, A., Gawley, D.: A new constrained parameter estimator for computer vision applications. Image and Vision Computing 22(2), 85–91 (2004)CrossRefGoogle Scholar
  9. 9.
    Georgel, P., Benhimane, S., Navab, N.: A unified approach combining photometric and geometric information for pose estimation. In: Proceedings of British Machine Vision Conference, pp. 133–142 (2008)Google Scholar
  10. 10.
    Rousseeuw, P.: Robust Regression and Outlier Detection. Wiley, Chichester (1987)zbMATHCrossRefGoogle Scholar
  11. 11.
    Obdrzalek, S., Matas, J.: Image retrieval using local compact DCT-based representation, pp. 490–497 (2003)Google Scholar
  12. 12.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  13. 13.
    Bookstein, F.L.: Fitting conic sections to scattered data. Computer Graphics and Image Processing 9(1), 56–71 (1979)CrossRefGoogle Scholar
  14. 14.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  15. 15.
    Weng, J., Huang, T., Ahuja, N.: Motion and structure from two perspective views: Algorithms, error analysis, and error estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(5), 451–476 (1989)CrossRefGoogle Scholar
  16. 16.
    Luong, Q., Deriche, R., Faugeras, O., Papadopoulo, T.: On determining the fundamental matrix: Analysis of different methods and experimental results (1993)Google Scholar
  17. 17.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65(1-2), 43–72 (2005)CrossRefGoogle Scholar
  18. 18.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761–767 (2004)CrossRefGoogle Scholar
  19. 19.
    Modersitzki, J.: Numerical Methods for Image Registration. Numerical Mathematics and Scientific Computation. Oxford University Press, Oxford (2004)zbMATHGoogle Scholar
  20. 20.
    Conn, A.R., Gould, N.I.M., Toint, P.L.: Trust-Region Methods. Society for Industrial and Applied Mathematics and Mathematical Programming Society (2000)Google Scholar
  21. 21.
    Riggi, F., Toews, M., Arbel, T.: Fundamental matrix estimation via TIP - transfer of invariant parameters. In: Proceedings of the International Conference on Pattern Recognition, Hong Kong, August 2006, pp. 21–24 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shufei Fan
    • 1
  • Rupert Brooks
    • 2
  • Frank P. Ferrie
    • 1
  1. 1.Center for Intelligent MachinesMcGill UniversityMontrealCanada
  2. 2.National Research Council CanadaIndustrial Materials InstituteMontrealCanada

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