Inspection of Coin Surface Defects using Multiple Eigen Spaces

다수의 고유 공간을 이용한 주화 표면 품질 진단

  • 김재민 (홍익대학교 전자전기공학부) ;
  • 류호진 (홍익대학교 전자전기공학부)
  • Received : 2011.01.06
  • Accepted : 2011.03.17
  • Published : 2011.03.28


In a manufacturing process of metal coins, surface defects of coins are manually detected. This paper describes an new method for detecting surface defects of metal coins on a moving conveyor belt using image processing. This method consists of multiple procedures: segmentation of a coin from the background, alignment of the coin to the model, projection of the aligned coin to the best eigen image space, and detection of defects by comparison of the projection error with an adaptive threshold. In these procedures, the alignement and the projection are newly developed in this paper for the detection of coin surface defects. For alignment, we use the histogram of the segmented coin, which converts two-dimensional image alignment to one-dimensional alignment. The projection reduces the intensity variation of the coin image caused by illumination and coin rotation change. For projection, we build multiple eigen image spaces and choose the best eigen space using estimated coin direction. Since each eigen space consists of a small number of eigen image vectors, we can implement the projection in real- time.


Defected Surface Inspection;Diffused Reflection Noise;Pattern Alignment;Adaptive Eigen Image


  1. T. S. Newan and A. K. Jain, "A survey of automated visual inspection," Computer Vision and Image Understanding, Vol. 61 Issue 2, pp.231-262, 1995.
  2. J. Wilder, "Finding and Evaluating Defects in Glass," Machine Vision for Inspection and Measurement, Academic Press, New York, 1989.
  3. A. Hamamatsu, H. Shibuya, Y. Oshima, S. Maeda, H. Nishiyama, and M. Noguchi, "Statistical Threshold Method for Semiconductor Wafer Inspection," 12th Asia-Pacific Conference on NDT, 2006.
  4. K. Choi, K. Koo, and J. S. Lee, "Development of Defect Classification Algorithm for POSCO Rolling Strip Surface Inspection System," SICE-ICASE International Joint Conference, 2006.
  5. F. R. Leta, F. F. Feliciano, and F.P.R. Martins, "Computer Vision System for Printed Circuit Board Inspection," ABCM Symposium Series in Mechatronics, Vol.3, pp.623-632, 2008.
  6. W. Y. Wu, M. J. Wang, and C. M. Liu, "Automated inspection of printed circuit boards through machine vision," Computers in Industry, Vol.28 Issue 2, pp.103-111, 1996.
  7. H. R. Yazdi and T. G. King, "Applications of 'vision in the loop' for inspection of lace fabric," Real-Time Imaging, Vol.4 Issue 5, pp.317-332. 1998.
  8. D. Tsai and R. Yang, "An Eigenvalue-based Similarity Measure and its Application in Defect Detection," Image and Vision Computing, Vol.23 No.12, pp.1094-101, 2005.
  9. A. Kumar and G. Pang, "Defect Detection in Textured Materials using Gabor Filters," IEEE Transactions on Industry Applications, Vol.38, pp.425-440, 2002.
  10. Z. Ibrahim and S. A. R. Al-Attas, "Wavelet-based printed circuit board inspection algorithm," Integrated Computer-Aided Engineering, Vol.12, pp.201-213, 2005.
  11. I. Matthews and S. Baker, "Active Appearance Models Revisited," International Journal of Computer Vision, Vol.60, No.2, pp.135-164, 2003.
  12. J. Yang and W. Du, "A Robust Hough Transform Algorithm for Determining the Radiation Centers of Circular and Rectangular Fields with Subpixel Accuracy," Physics in Medicine and Biology, Vol.54, No.3, pp.555-567, 2009.
  13. J. Q. Li and A. R. Barron, "Mixture Density Estimation," In Advances in Neural Information Processing Systems, Vol.12, pp.279-285, 1999.