DOI QR코드

DOI QR Code

서브픽셀 에지를 이용한 정밀한 에지기반 정합

An Accurate Edge-Based Matching Using Subpixel Edges

  • 조태훈 (한국기술교육대학교 정보기술공학부)
  • 발행 : 2007.05.01

초록

In this paper, a 2-dimensional accurate edge-based matching algorithm using subpixel edges is proposed that combines the Generalized Hough Transform(GHT) and the Chamfer matching to complement the weakness of either method. First, the GHT is used to find the approximate object positions and orientations, and then these positions and orientations are used as starting parameter values to find more accurate position and orientation using the Chamfer matching with distance interpolation. Finally, matching accuracy is further refined by using a subpixel algorithm. Testing results demonstrate that greater matching accuracy is achieved using subpixel edges rather than edge pixels.

키워드

참고문헌

  1. L. G. Brown, 'A survey of image registration techniques,' ACM Computing Surveys, vol. 24, no. 4, pp. 325-376, 1992 https://doi.org/10.1145/146370.146374
  2. S. L. Tanimoto, 'Template matching in pyramids,' Computer Graphics and Image Processing, vol. 16, pp. 356- 369, 1981 https://doi.org/10.1016/0146-664X(81)90046-0
  3. H. G. Barrow, J. M. Tenenbaum, R C. Bolles, and H. C. Wolf, 'Parametric correspondence and Chamfer matching: Two new techniques for image matching,' Proc. 5th Int. Joint Can! Artificial Intelligence, pp. 659-663, Cambridge, MA, 1977
  4. G. Borgefors, 'Hierarchical Chamfer matching: a parametric edge matching algorithm,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 849-865, 1988 https://doi.org/10.1109/34.9107
  5. D. Chetverikov and Y. Khenokh, 'Matching for shape defect detection,' LNCS 1689, pp. 367-374, 1999
  6. D. Gavila, 'Pedestrian detection from a moving vehicle,' Proc. ECCV, pp. 37-49, 2000 https://doi.org/10.1007/3-540-45053-X_3
  7. A. Thayananthan, B. Stenger, P. H. S. Torr, and R. Cipolla, 'Shape context and Chamfer matching in cluttered scenes,' Proc. CVPR 2003, Madison, Wisconsin, pp. 127-135, 2003
  8. W. J. Rucklidge, 'Efficiently locating objects using the Hausdorff distance,' International Journal of Computer Vision, vol. 24, no. 3, pp. 251-270, 1997 https://doi.org/10.1023/A:1007975324482
  9. C. F. Olson and D. P. Huttenlocher, 'Automatic target recognition by matching oriented edge pixels,' IEEE Trans. on Image Processing, vol. 6, no. 1, pp. 103-113, 1997 https://doi.org/10.1109/83.552100
  10. D. H. Ballard, 'Generalizing Hough transform to detect arbitrary shapes,' Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981 https://doi.org/10.1016/0031-3203(81)90009-1
  11. M. Ulrich, C. Steger, A. Baumgartner, and H. Ebner, 'Real-time object recognition in digital images for industrial applications,' 5th Conf. on Optical 3-D Measurement Techniques, Vienna, pp. 308-318, 2001
  12. J. Canny, 'A computational approach to edge detection,' IEEE Trans. Pattern Analy. and Mach. Intelli., vol. 8, no. 6, pp. 679-698, 1986 https://doi.org/10.1109/TPAMI.1986.4767851
  13. E. R. Davies, Machine Vision, 3rd Ed., Morgan Kaufinann, 2005
  14. R. Jain, R. Kasturi, and E.G. Schunck, Machine Vision, McGraw-Hill, 1995
  15. N. Otsu, 'A threshold selection method from gray-level histograms,' IEEE Trans. Systems, Man, Cybernetics, vol. SMC-9, no. 1, pp. 62-66, 1979
  16. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C, 2nd Ed., Cambridge University Press, 1992