Improved Chamfer Matching Using Interpolated Chamfer Distance and Subpixel Search

  • Tai-Hoon Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

Chamfer matching is an edge based matching technique that has been used in many applications. The matching process is to minimize the distance between transformed model edges and image edges. This distance is usually computed at the pixel resolution using a distance transform, thus reducing accuracy of the matching. In this paper, an improved approach for accurate chamfer matching is presented that uses interpolation in the distance calculation for subpixel distance evaluation. Also, instead of estimating the optimal position in subpixel using a neighborhood of the pixel position with the minimum distance, for more accurate matching, we use the Powell’s optimization to find the distance minimum through actual distance evaluations in subpixel. Experimental results are presented to show the validity of our approach.

Keywords

Edge Distance Edge Pixel Distance Image Distance Evaluation Accurate Match 
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.

References

  1. 1.
    Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: Proc. 5th Int. Joint Conf. Artificial Intelligence, Cambridge, MA, pp. 659–663 (1977)Google Scholar
  2. 2.
    Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence 10(6), 849–865 (1988)CrossRefGoogle Scholar
  3. 3.
    Chetverikov, D., Khenokh, Y.: Matching for Shape Defect Detection. In: Solina, F., Leonardis, A. (eds.) CAIP 1999. LNCS, vol. 1689, pp. 367–374. Springer, Heidelberg (1999)Google Scholar
  4. 4.
    Gavrila, D.M.: Pedestrian Detection from a Moving Vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: Proc. CVPR 2003, Madison, Wisconsin, pp. 127–135 (2003)Google Scholar
  6. 6.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C, 2nd edn. Cambridge University Press, Cambridge (1992)MATHGoogle Scholar
  7. 7.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Analy. and Mach. Intelli. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  8. 8.
    Davies, E.R.: Machine Vision, 3rd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar
  9. 9.
    Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. McGraw-Hill, New York (1995)Google Scholar
  10. 10.
    Ballard, D.H.: Generalizing Hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)MATHCrossRefGoogle Scholar
  11. 11.
    Borgefos, G.: Distance transformations in digital images. Computer Vision, Graphics, and Image Processing 34(3), 344–371 (1986)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Tai-Hoon Cho
    • 1
  1. 1.School of Information Technology, Korea University of Technology and Education, 307 Gajun-ri, Byungchun-myun, Chonan, ChoongnamKorea

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