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Angle aided circle detection based on randomized Hough transform and its application in welding spots detection

1 College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China
2 National Engineering Laboratory for Robot Vision Perception and Control Technologies, Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, Hunan, China
3 Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, L1H 7K4, Canada
4 Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada

Special Issues: State-of-the-art strategies to tackle emerging contaminants of high concern: In greening the 21st - century environmental engineering

The Hough transform has been widely used in image analysis and digital image processing due to its capability of transforming image space detection to parameter space accumulation. In this paper, we propose a novel Angle-Aided Circle Detection (AACD) algorithm based on the randomized Hough transform to reduce the computational complexity of the traditional Randomized Hough transform. The algorithm ameliorates the sampling method of random sampling points to reduce the invalid accumulation by using region proposals method, and thus significantly reduces the amount of computation. Compared with the traditional Hough transform, the proposed algorithm is robust and suitable for multiple circles detection under complex conditions with strong anti-interference capacity. Moreover, the algorithm has been successfully applied to the welding spot detection on automobile body, and the experimental results verifies the validity and accuracy of the algorithm.
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Keywords angle aided randomized Hough transform; image processing; machine vision; circle detection; welding spot

Citation: Qiaokang Liang, Jianyong Long, Yang Nan, Gianmarc Coppola, Kunlin Zou, Dan Zhang, Wei Sun. Angle aided circle detection based on randomized Hough transform and its application in welding spots detection. Mathematical Biosciences and Engineering, 2019, 16(3): 1244-1257. doi: 10.3934/mbe.2019060


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