한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제17권1호
  • /
  • Pages.94-99
  • /
  • 2007
  • /
  • 1976-9172(pISSN)
  • /
  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Generalized Hough Transform과 Chamfer 정합을 이용한 에지기반 정합

Edge-Based Matching Using Generalized Hough Transform and Chamfer Matching

  • 조태훈 (한국기술교육대학교 정보기술공학부)
  • 발행 : 2007.02.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 Generalized Hough Transform (GHT)와 Chamfer 정합(Chamfer matching)방법을 결합하여, 두 방법의 약점을 보완하는 새로운 이차원 에지기반 매칭기법이 제시된다. 먼저, GHT를 적용하여, 물체의 대략적인 위치와 방향을 추정하고, 이를 시작점으로 하여, 보다 정확한 위치와 방향을 Chamfer 정합기법을 적용하여 찾았다. 끝으로, 서브픽셀(subpixel) 알고리즘을 사용하여, 매칭정확도를 향상시켰다. 제안된 알고리즘은 다양한 전자부품 영상에 대해 실험한 결과 좋은 결과를 나타내었다.

In this paper, a 2-dimensional edge-based matching algorithm is proposed that combines the generalized Hough transform (GHT) and the Chamfer matching to complement weakness of either method. First, the GHT is used to find approximate object positions and orientations, and then these positions and orientations are used as starling parameter values to find more accurate position and orientation using the Chamfer matching. Finally, matching accuracy is further refined by using a subpixel algorithm. The algorithm was implemented and successfully tested on a number of images containing various electronic components.

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 Conf. Artificial Intelligence, Cambridge, MA, pp.659-663, 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. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. E.R Davies, Machine Vision, 3rd Ed., Morgan Kaufmann, 2005
  11. R Jain, R Kasturi, and E.G. Schunck, Machine Vision, McGraw-Hill, 1995
  12. N. Otsu, 'A threshold selection method from gray-level histograms,' IEEE Trans. Systems, Man, Cybernetics, voI.SMC-9, no.1, pp.62-66, 1979
  13. P.V.C. Hough, 'Method and means for recognizing complex patterns,' US Paternt 3069654, 1962