Comparison of Hough Transform and Mean Shift Algorithm for Estimation of the Orientation Angle of Industrial Data Matrix Codes
In automatic manufacturing and assembling of mechanical, electrical and electronic parts one needs to reliably identify the position of components and to extract the information of these components. Data Matrix Codes (DMC) are established by these days in many areas of industrial manufacturing thanks to their concentration of information on small spaces. In today’s usually order-related industry, where increased tracing requirements prevail, they offer further advantages over other identification systems. This underlines in an impressive way the necessity of a robust code reading system for detecting DMC on the components in factories. This paper compares two methods for estimating the angle of orientation of Data Matrix Codes: one method based on the Hough Transform and the other based on the Mean Shift Algorithm. We concentrate on Data Matrix Codes in industrial environment, punched, milled, lasered or etched on different materials in arbitrary orientation.
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[8] Rob L. Hyndman, Xibin Zhang, and Maxwell L. King, "Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC," 2004.
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[11] Ezio Malis and Eric Marchand, "Experiments with robust estimation techniques in real-time robot vision: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on DOI - 10.1109/IROS.2006.282572," Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pp. 223-228, 2006.
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[14] Yefeng Zheng, B. Georgescu, Haibin Ling, S. K. Zhou, M. Scheuering, and D. Comaniciu, "Constrained marginal space learning for efficient 3D anatomical structure detection in medical images: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on DOI - 10.1109/CVPR.2009.5206807," Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 194-201, 2009.
[1] INTERNATIONAL STANDARD, "Information Technology - International symbology specification - Data matrix.,
[2] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man and Cybernetics, DOI - 10.1109/TSMC.1979.4310076 (Systems, Man and Cybernetics, IEEE Transactions on), vol. 9, no. 1, pp. 62-66, 1979.
[3] Ye Zhang, Hongsong Qu, and Yanjie Wang, "Adaptive Image Segmentation Based on Fast Thresholding and Image Merging," Artificial Reality and Telexistence - Workshops, 2006. ICAT'06. 16th International Conference on, pp. 308-311, 2006.
[4] A. Bovik, Ed., The Essential Guide to Image Processing. Elsevier, 2009.
[5] R. O. Duda and P. E. Hart, Use of the Hough Transformation to Detect Lines and Curves in Pictures. Comm. ACM, January, 1972.
[6] D. Comaniciu and P. Meer, "Mean shift: a robust approach toward feature space analysis: Pattern Analysis and Machine Intelligence, IEEE Transactions on," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, no. 5, pp. 603-619, 2002.
[7] Yizong Cheng, "Mean shift, mode seeking and clustering: Pattern Analysis and Machine Intelligence, IEEE Transactions on," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 17, no. 8, pp. 790-799, 1995.
[8] Rob L. Hyndman, Xibin Zhang, and Maxwell L. King, "Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC," 2004.
[9] V. C. Raykar and D. R.," Fast optimal bandwidth selection for kernel density estimation: Proceedings of the sixth SIAM International Conference on Data Mining,"Proceedings of the sixth SIAM International Conference on Data Mining, pp. 524-528, 2006.
[10] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking: Pattern Analysis and Machine Intelligence, IEEE Transactions on," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, no. 5, pp. 564-577, 2003.
[11] Ezio Malis and Eric Marchand, "Experiments with robust estimation techniques in real-time robot vision: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on DOI - 10.1109/IROS.2006.282572," Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pp. 223-228, 2006.
[12] H. Guo, "A Simple Algorithm for Fitting a Gaussian Function (DSP Tips and Tricks): Signal Processing MAgazine, IEEE," Signal Processing MAgazine, IEEE, vol. 28, no. 5, pp. 134-137, 2011.
[13] J. Princen, H. K. Yuen, J. Illingworth, and J. Kittler, "A comparison of Hough transform methods: Image Processing and its Applications, 1989., Third International Conference on: Image Processing and its Applications, 1989., Third International Conference on DOI -," Image Processing and its Applications, 1989., Third International Conference on: pp. 73-77, 1989.
[14] Yefeng Zheng, B. Georgescu, Haibin Ling, S. K. Zhou, M. Scheuering, and D. Comaniciu, "Constrained marginal space learning for efficient 3D anatomical structure detection in medical images: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on DOI - 10.1109/CVPR.2009.5206807," Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 194-201, 2009.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:58419", author = "Ion-Cosmin Dita and Vasile Gui and Franz Quint and Marius Otesteanu", title = "Comparison of Hough Transform and Mean Shift Algorithm for Estimation of the Orientation Angle of Industrial Data Matrix Codes", abstract = "In automatic manufacturing and assembling of mechanical, electrical and electronic parts one needs to reliably identify the position of components and to extract the information of these components. Data Matrix Codes (DMC) are established by these days in many areas of industrial manufacturing thanks to their concentration of information on small spaces. In today’s usually order-related industry, where increased tracing requirements prevail, they offer further advantages over other identification systems. This underlines in an impressive way the necessity of a robust code reading system for detecting DMC on the components in factories. This paper compares two methods for estimating the angle of orientation of Data Matrix Codes: one method based on the Hough Transform and the other based on the Mean Shift Algorithm. We concentrate on Data Matrix Codes in industrial environment, punched, milled, lasered or etched on different materials in arbitrary orientation.
", keywords = "Industrial data matrix code, Hough transform, mean shift.", volume = "6", number = "4", pages = "838-7", }
