Advertisement

A Hybrid Approach for Robust Corner Matching

  • Fanhuai Shi
  • Xixia Huang
  • Ye Duan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 88)

Abstract

Robust and high accuracy corner matching plays an essential role in many applications in computer vision such as camera calibration, 3D reconstruction and robot localization. In this paper, we describe a hybrid approach that can automatically detect and match image corners with high accuracy. Our approach is based on SIFT structure information and sub-pixel Harris corner localization, which is rotation invariant and is localized directly on true image corners detected by the enhanced curvature scale space method. Experimental results show that the proposed method offers an effective solution to automatic robust corner matching.

Keywords

Corner Point Camera Calibration Sift Feature Robot Localization Epipolar Geometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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
  2. 2.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  3. 3.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  4. 4.
    He, X.C., Yung, N.H.C.: Curvature scale space corner detector with adaptive threshold and dynamic region of support. In: Proc. of ICPR, pp. 791–794 (2004)Google Scholar
  5. 5.
    Jacob, M., Unser, M.: Design of steerable filters for feature detection using Canny-like criteria. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1007–1019 (2004)CrossRefGoogle Scholar
  6. 6.
    Lindeberg, T.: Feature detection with automatic scale selection. Inter. J. Comput. Vision 30(2), 79–116 (1998)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Inter. J. Comput. Vision 2(60), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple object class detection with a generative model. In: Proc of CVPR 2006, pp. 26–36 (2006)Google Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Inter. J. Comput. Vision 1(60), 63–86 (2004)CrossRefGoogle Scholar
  10. 10.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  11. 11.
    Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 756–770 (2004)CrossRefGoogle Scholar
  12. 12.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proc of ICCV 2003, pp. 1470–1478 (2003)Google Scholar
  13. 13.
    Tuytelaars, T., Mikolajczyk, K.: Local Invariant Feature Detectors: A Survey. Foundations and Trends in Computer Graphics and Vision 3(3), 177–280 (2007)CrossRefGoogle Scholar
  14. 14.
    Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.-T.: A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence Journal 78(1-2), 87–119 (1995)CrossRefGoogle Scholar
  15. 15.
    Zhao, F., Huang, Q., Gao, W.: Image Matching by Normalized Cross-Correlation. In: Proc. of ICASSP 2006, Toulouse, France, May 14-19, vol. 2, pp. 729–732 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fanhuai Shi
    • 1
  • Xixia Huang
    • 2
  • Ye Duan
    • 3
  1. 1.Welding Engineering InstituteShanghai Jiao Tong UniversityChina
  2. 2.Marine Technology & Control Engineering Key LabShanghai Maritime UniversityChina
  3. 3.Computer Science DepartmentUniversity of Missouri-ColumbiaUSA

Personalised recommendations