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http://dx.doi.org/10.5762/KAIS.2012.13.5.1996

Visual Inspection System for Irregularly Formed Timing Belt with Low Reflection Ratio  

Lee, Jae-Woo (School of Creative Science and Engineering, Waseda University)
Yoon, Joong-Sun (School of Mechanical Engineering, Pusan National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.5, 2012 , pp. 1996-2001 More about this Journal
Abstract
Visual inspection systems are widely proposed for the well formed surface materials like electronics parts. But the materials with ill reflection ability have many troubles when visual inspection system is introduced. We have developed a robust visual inspection system that can work well in spite of low reflection ratio and with much noise when truth model is not known in the mixed production line. A workpiece identification technique using k-means has been proposed to identify the type. Based on the identified type, a robust-to-noise segmentation method, called active contour, has been applied to segment the features from the image. Finally, Kalman filter has been applied to adapt the error variation. Experiment shows that performance is about to match the accuracy of manual measurement using projectors.
Keywords
Visual Inspection System; Low Reflection Ratio; Active Contour Segmentation; Kalman Filter;
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  • Reference
1 A. P. Dempster, N. M. Laird and D.B. Rubin, "Maximum Likelihood Estimation from Incomplete Data via the EM Algorithm", Journal of Royal Statistical Society, vol. 39, no. 1, pp. 1-38, 1977.
2 H. Gu, G.-D. Su, C. Du, "Fuzzy and ISODATA Classification of Face Contours", Proceedings of International Conference of Machine Learning and Cybernetics, 26-29, August, 2004.
3 R. O. Duda and P. E. Hart, "Use of the Hough Transform to Detect Lines and Curves in Pictures", Comm. ACM, vol. 15, no. 1, pp. 11-15, Jan. 1972.   DOI
4 M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active Contour Models", International Conference on Computer Vision, pp. 259-268, 1987.
5 A. A. Amini, T. E. Weymouth, and R. C. Jain, "Using Dynamic Programming for Solving Variational Problems in Vision", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 9, pp. 855-867, 1990.   DOI
6 S. J. Julier and J. K. Uhlmann, "A New Extension of the Kalman Filter to Nonlinear Systems", Proceedings of International Symposium on Aerospace/Defense Sensing, Simulation and Controls, 1997.
7 R. O. Duda, P. E. Hart and D. G. Stork, Pattern Recognition, 2nd Edition, Wiley and Sons, pp. 27-30, 2000.
8 C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2009.
9 G. Bradsky and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly Media, 1st edition, 2008.