Circle detection based on artificial bee colony algorithm
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- Category: Information technologies, systems analysis and administration
- Last Updated on 04 August 2016
- Published on 04 August 2016
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Authors:
Shuang Zhang, School of Mechanical Science and Engineering, Jilin University, Changchun, Jilin, China, School of Mechatronic Engineering, Changchun Institute of Technology, Changchun, Jilin, China
Xiaoqin Zhou, School of Mechatronic Engineering, Changchun Institute of Technology, Changchun, Jilin, China
Yiqiang Wang, School of Mechatronic Engineering, Changchun Institute of Technology, Changchun, Jilin, China
Jingang Gao, School of Mechatronic Engineering, Changchun Institute of Technology, Changchun, Jilin, China
Hua Wang, Graduate Department, Changchun Institute of Technology, Changchun, Jilin, China
Abstract:
Purpose. This paper presents an algorithm for the automatic detection of multiple circular shapes from complicated and a noisy image, which does not take into consideration the conventional-Hough transform principles with large amount of calculation.
Methodology. The approach is based on the artificial bee colony algorithm, a swarm optimization algorithm inspired by the intelligent for aging behavior of honey bees. A new objective function has been derived for the edge map of a desired image.
Findings. A matching function determines if such circle candidates are actually present in the image. By the use of artificial bee colony algorithm the objective function is minimized and this leads to automatic circle detection in digital images. The proposed method is able to detect single or multiple circles from a digital image through only one optimization.
Originality. The industrial product calibration card image is chosen as actual image to test the approach which includes a circular-shape object of different sizes in each image. The research on industrial product has not been found at present.
Practical value. Compared with Hough transform algorithm, the amount of calculation and the occupied memory space is reducing greatly, therefore operation speed is improved. The experimental results show that the improved algorithm meets the requirements of industrial real-time detection with a good application effect.
Список літератури / References
1. Ming Chen, Feng Zhuang, Zhenhong Du, and Renyi Liu, 2013. Circle detection using scan lines and histograms, Optical Review, Vol. 20, No. 6, pp. 484–490.
2. Erik Cuevas, Felipe Sención-Echauri, Daniel Zaldivar and Marco Pérez-Cisneros , 2012. Multi-circle detection on images using artificial bee colony (ABC) optimization, Soft Computing, Vol. 16, No. 2, pp. 281–296.
3. Erik Cuevas, Fernando Wario, Valentín Osuna-Enciso, Daniel Zaldivar and Marco Pérez-Cisneros, 2012. Fast algorithm for multiple-circle detection on images using learning automata, IET Image Processing, Vol. 6, No. 8, pp. 1124–1135.
4. Elif Deniz Yigitbasi, Nurdan Akhan Baykan, 2013. Edge Detection using Artificial Bee Colony Algorithm, International Journal of Information and Electronics Engineering, Vol. 3, No. 6, pp. 634–638.
5. Saeid Fazli, and Saeid Fathi Ghiri, 2012. Automatic Circle Detection in Digital Imagesusing Artificial Bee Colony Algorithm, International Conference on Advances in Computer and Electrical Engineering, pp. 21–23.
6. D. Narayana Reddy, Mohan A. R, Subhramanya Bhat (2014), “Canny Edge Detection using Verilog”, International Journal of Engineering Sciences & Research Technology, Vol. 3, No. 6, pp. 256–260.
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