Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Robust PCB Image Alignment using SIFT

잡음과 회전에 강인한 SIFT 기반 PCB 영상 정렬 알고리즘 개발

  • Received : 2010.01.26
  • Accepted : 2010.05.01
  • Published : 2010.07.01
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Abstract

This paper presents an image alignment algorithm for application of AOI (Automatic Optical Inspection) based on SIFT. Since the correspondences result using SIFT descriptor have many wrong points for aligning, this paper modified and classified those points by five measures called the CCFMR (Cascade Classifier for False Matching Reduction) After reduced the false matching, rotation and translation are estimated by point selection method. Experimental results show that the proposed method has fewer fail matching in comparison to commercial software MIL 8.0, and specially, less than twice with the well-controlled environment’s data sets (such as AOI system). The rotation and translation accuracy is robust than MIL in the noise data sets, but the errors are higher than in a rotation variation data sets although that also meaningful result in the practical system. In addition to, the computational time consumed by the proposed method is four times shorter than that by MIL which increases linearly according to noise.

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References

  1. C. S. Chen et al., "A novel fourier descriptor based image alignment algorithm for automatic optical inspection," Journal of Visual Communications and Representation, 2008.
  2. D. G. Lowe, "Distinctive image features from scale invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  3. Y. Y. Tang, H. D. Cheng, and C. Y. Suen, "Transformation-ringprojection algorithm and its VLSI implementation," International Journal of Pattern Recognition and Artificial Intelligence, vol. 5, pp. 25-56, 1991. https://doi.org/10.1142/S0218001491000053
  4. D. M. Tsai and C. H. Chiang, "Rotation-invariant pattern matching using wavelet decomposition," Pattern Recognition Letters, vol. 23, pp. 191-201, 2002. https://doi.org/10.1016/S0167-8655(01)00099-X
  5. M. S. Choi and W. Y. Kim, "A novel two stage template matching method for rotation and illumination invariance," Pattern Recognition, vol. 35, pp. 119-129, 2002. https://doi.org/10.1016/S0031-3203(01)00025-5
  6. D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, "Comparing images using the Hausdorff distance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, 1993. https://doi.org/10.1109/34.232073
  7. D. P. Huttenlocher, R. H. Lilien, and C. F. Olson, "View-based recognition using an eigenspace approximation to the Hausdorff measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 951-955, 1999. https://doi.org/10.1109/34.790437
  8. O. K. Kwon, D. G. Sim, and R. H. Park, "Robust Hausdorff distance matching algorithms using pyramidal structures," Pattern Recognition, vol. 34, no. 10, pp. 2005-2013, 2001. https://doi.org/10.1016/S0031-3203(00)00132-1
  9. D. Zhang and G. Lu, "A compartive study of curvatue scale space an Fourier descriptor," Journal of Visual Communication & Image Representation, vol. 14, pp. 41-60, 2003.
  10. C. J. Chen, S. H. Lai, S. W. Liu, T. Ku, and S. Y. C. Yeh, "Optical PCB inspection system based on Hausdorff distance," Machine Vision Applications in Industrial Inspection, pp. 53-61, 2005.
  11. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 1615-1630, 2005.
  12. Y. Ke and R. Sukthankar, "PCA-SIFT: A more distinctive representation for local image descriptors," Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp 511-517, 2004.
  13. H. Bay, Y. Tuytelaars, and G. L. Van, "SURF: Speeded up robust features," Computer Vision and Image Understanding, vol. 110, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  14. E. N. Mortensen, D. Hongli, and L. Shapiro, "A SIFT descriptor with global context," Proc. of the Conference on Computer Vision and Pattern Recognition, pp. 184-190, 2005.
  15. J. S. Beis and D. G. Lowe, "Shape indexing using approximate nearest-neighbour search in high-dimensional spaces," Computer Vision and Pattern Recognition, pp. 1000-1006, 1997.
  16. M. Brown and D. G. Lowe, "Invariant features from interest point groups," Proc. of the British Machine Vision Conference, vol. 656, 2002.
  17. Matrox Imaging, http://www.matrox.com/, 2009.