Advertisement

Efficient NCC-Based Image Matching in Walsh-Hadamard Domain

  • Wei-Hau Pan
  • Shou-Der Wei
  • Shang-Hong Lai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)

Abstract

In this paper, we proposed a fast image matching algorithm based on the normalized cross correlation (NCC) by applying the winner-update strategy on the Walsh-Hadamard transform. Walsh-Hadamard transform is an orthogonal transformation that is easy to compute and has nice energy packing capability. Based on the Cauchy-Schwarz inequality, we derive a novel upper bound for the cross-correlation of image matching in the Walsh-Hadamard domain. Applying this upper bound with the winner update search strategy can skip unnecessary calculation, thus significantly reducing the computational burden of NCC-based pattern matching. Experimental results show the proposed algorithm is very efficient for NCC-based image matching under different lighting conditions and noise levels.

Keywords

pattern matching image matching image alignment normalized cross correlation winner update 

References

  1. 1.
    Zhu, S., Ma, K.K.: A new diamond search algorithm for fast block matching motion estimation. IEEE Trans. Image Processing 9(2), 287–290 (2000)CrossRefGoogle Scholar
  2. 2.
    Li, R., Zeng, B., Liou, M.L.: A new three-step search algorithm for block motion estimation. IEEE Trans. Circuits Systems Video Technology 4(4), 438–442 (1994)CrossRefGoogle Scholar
  3. 3.
    Po, L.M., Ma, W.C.: A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 6, 313–317 (1996)CrossRefGoogle Scholar
  4. 4.
    Li, W., Salari, E.: Successive elimination algorithm for motion estimation. IEEE Trans. Image Processing 4(1), 105–107 (1995)CrossRefGoogle Scholar
  5. 5.
    Gao, X.Q., Duanmu, C.J., Zou, C.R.: A multilevel successive elimination algorithm for block matching motion estimation. IEEE Trans. Image Processing 9(3), 501–504 (2000)CrossRefGoogle Scholar
  6. 6.
    Lee, C.-H., Chen, L.-H.: A fast motion estimation algorithm based on the block sum pyramid. IEEE Trans. on Image Processing 6(11), 1587–1591 (1997)CrossRefGoogle Scholar
  7. 7.
    Gharavi-Alkhansari, M.: A fast globally optimal algorithm for template matching using low-resolution pruning. IEEE Trans. Image Processing 10(4), 526–533 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hel-Or, Y., Hel-Or, H.: Real-time pattern matching using projection kernels. IEEE Trans. Pattern Analysis Machine Intelligence 27(9), 1430–1445 (2005)CrossRefGoogle Scholar
  9. 9.
    Chen, Y.S., Huang, Y.P., Fuh, C.S.: A fast block matching algorithm based on the winner-update strategy. IEEE Trans. Image Processing 10(8), 1212–1222 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Di Stefano, L., Mattoccia, S.: Fast template matching using bounded partial correlation. Machine Vision and Applications 13(4), 213–221 (2003)CrossRefGoogle Scholar
  11. 11.
    Di Stefano, L., Mattoccia, S.: A Sufficient Condition based on the Cauchy-Schwarz Inequality for Efficient Template Matching. In: IEEE International Conf. Image Processing, Barcelona, Spain, September 14-17 (2003)Google Scholar
  12. 12.
    Lewis, J.P.: "Fast template matching," Vision Interface, pp. 120–123 (1995)Google Scholar
  13. 13.
    Mc Donnel, M.: Box-filtering techniques. Computer Graphics and Image Processing 17, 65–70 (1981)CrossRefGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 52(2), 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vision Computing 21(11), 977–1000 (2003)CrossRefGoogle Scholar
  16. 16.
    Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  17. 17.
    Mahmood, A., Kahn, S.: Exploiting Inter-frame Correlation for Fast Video to Reference Image Alignment. In: Proc. 8th Asian Conference on Computer Vision (2007)Google Scholar
  18. 18.
    Pele, O., Werman, M.: Robust real time pattern matching using Bayesian sequential hypothesis testing. IEEE Trans. Pattern Analysis Machine Intelligence (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wei-Hau Pan
    • 1
  • Shou-Der Wei
    • 1
  • Shang-Hong Lai
    • 1
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

Personalised recommendations