Noise Elimination for Image Subtraction in Printed Circuit Board Defect Detection Algorithm
DOI:
https://doi.org/10.24297/ijct.v10i2.7000Keywords:
Printed Circuit Boards, Image Registration, Affine Transformation, Bi-cubic Interpolation, Defect DetectionAbstract
Image subtraction operation has been frequently used for automated visual inspection of printed circuit board (PCB) defects. Even though the image subtraction operation able to detect all defects occurred on PCB, some unwanted noise could be detected as well. Hence, before the image subtraction operation can be applied to real images of PCB, image registration operation should be done to align a defective PCB image against a template PCB image. This study shows how the image registration operation is incorporated with a thresholding algorithm to eliminate unwanted noise. The results show that all defects occurred on real images of PCB can be correctly detected without interfere by any unwanted noise.
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References
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[2] W.Y. Wu, M.J. Wang and C.M. Liu. 1996. Automated Inspection of Printed Circuit Boards Through Machine Vision, Computers in Industry, vol.28, no.2, pp.103-111.
[3] J.H. Koo and S.I. Yoo. 1998. A Structural Matching for Two-dimensional Visual Pattern Inspection, IEEE International Conference on Systems, Man, and Cybernetics, vol.5, pp.4429-4434.
[4] C.R. Charette, S. Park, R. Williams, B. Benhabi and K.C. Smith. 1988. Development and Integration of a Microcomputer based Image Analysis System for Automatic PCB Inspection, Proceedings of International Conference on Computer Integrated Manufacturing, pp.129-135.
[5] L. Brown. 1992. A Survey of Image Registration Techniques, ACM Computing Surveys, vol.24, no.4, pp.325-376.
[6] R.M. Haralick, S.R. Sternberg, and X. Zhuang. 1987. Image Analysis using Mathematical Morphology, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, no.4, pp. 532-550, 1987.
[7] Ismail Ibrahim, Syed Abdul Rahman Syed Abu Bakar, Musa Mohd Mokji, Kamal Khalil, Zulkifli Md Yusof, Jameel Abdulla Ahmed Mukred, Mohd Saberi Mohamed, Zuwairie Ibrahim, An Image Registration Technique to Enhance PCB Inspection Algorithm with Real Images, ICIC Express Letters, vol.6, no.3, pp. 717-721, 2012.
[8] M. Sezgin and B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging, vol.13, no.1, pp. 146-156, 2004.
[9] I. Ibrahim, S.A.R Abu-Bakar, M.M. Mokji, K. Khalil, Z.M. Yusof, and Z. Ibrahim. 2011. Performance measurement of thresholding algorithms in printed circuit board inspection system. 3rd International Conference on Computational Intelligence, Modelling and Simulation.
[10] J.M.S. Prewitt and M.L. Mendelsohn. 1966. The analysis of cell images, Annals of the New York Academy of Science, vol. 128, no. 3, pp. 1035-1053.
[11] J. Weszka and A. Rosenfeld. 1979. Histogram modification for threshold selection, IEEE Transaction on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 38–52.
[12] W. Doyle. 1962. Operation useful for similarity-invariant pattern recognition, Journal of the Association for Computing Machinery, vol.9, no.2, pp. 259-267.
[13] C.A. Glasbey. 1993. An analysis of histogram-based thresholding algorithms, Graphical Models and Image Processing (CVGIP 1993), vol. 55, no. 6, pp. 532-537.
[14] W. Tsai. 1985. Moment-preserving thresholding: a new approach, Computer Vision, Graphics, and Image Processing, vol.29, no.3, pp. 377-393.
[15] J.N. Kapur, P.K. Sahoo and A.K.C. Wong. 1985. A new method for gray-level picture thresholding using the entropy of the histogram, Graphical Models and Image Processing, vol.29, no.3, pp. 273-285.
[16] N. Otsu. 1979. A threshold selection method from gray-level histogram, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, no.2, pp. 62-66.
[17] T. Ridler and S. Calvard. 1978. Picture thresholding using an iterative selection method, IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 8, pp. 630-632.
[18] J. Kittler and J. Illingworth. 1986. Minimum error thresholding, Pattern Recognition, vol. 19, no. 1, pp. 41-47.
[19] Y. Bazi, L. Bruzzone and F. Melgani. 2007. Image thresholding based on the EM algorithm and generalized gaussian distribution, Pattern Recognition, vol. 40, no. 2, pp. 619-634.
[20] A. Hussain, M.A. Jaffar and A.M. Mirza. 2011. Random-Valued Impulse Noise Removal Using Fuzzy Logic, International Journal of Innovative Computing, Information and Control, vol. 6, no. 10, pp. 4273-4288.
[21] R.C Gonzalez and P. Wintz. 1987. Digital image processing, Addison-Wesley Publishing Company, Inc.
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Published
2013-08-05
How to Cite
Ibrahim, Z., Ibrahim, I., Khalil, K., Nawawi, S. W., Abdul Rahim, M. A., Aspar, Z., & Wan Ahmad, W. K. (2013). Noise Elimination for Image Subtraction in Printed Circuit Board Defect Detection Algorithm. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 10(2), 1317–1329. https://doi.org/10.24297/ijct.v10i2.7000
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