Abstract
Circle Hough transform (CHT) is the most commonly used method to inspect circular shapes for its advantage in strong robustness. However, it requires large amounts of storage and computing power, which cannot meet the requirements of real-time processing. To overcome this deficiency, this paper presents a novel circle detection method based on adaptive artificial fish swarm algorithm (AAFSA) by determining the circle center and the radius of circular parts. A new fitness function had been developed to evaluate the similarity of a candidate circle with a real circle. Based on the fitness values, a batch of encoded candidate circles is modified through the AAFSA in order that they can match with the actual circles on the edge map. Experiments results show our proposed method can accurately detect circular parts by search the optimum values in the parameter space. Compared to other popular approaches, i.e., the CHT, the least square method (LS) and the random sample consensus (RANSAC), the proposed method achieved a remarkable improvement in both accuracy and speed of circular parts detection.
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References
Davies, E.R.: Design of cost-effective systems for the inspection of certain food products during manufacture. In: Proceeding of. 4th Conference on Robot Vision and Sensory Controls, pp. 437–446 (1984)
Davies, E.R.: Radial histograms as an aid in the inspection of circular objects. In: IEEE Proceedings of the Control Theory and Applications [see also IEEE Proceedings-Control Theory and Applications], vol. 132, pp. 158–163 (1985)
Jin, T.U., Sheng-Deng, W.U., Cao, J.W., Shi-Lun, L.I.: Research on measure method of diameter of partial circle based on CCD. Semicon. Technol. 32, 573–574 (2007)
Mi, J.X., Huang, D.S., Wang, B., Zhu, X.J.: The nearest-farthest subspace classification for face recognition. Neurocomputing 113, 241–250 (2013)
Wang, B., Shen, H., Fang, A., Huang, D.S., Jiang, C., Zhang, J., Chen, P.: A regression model for calculating the second dimension retention index in comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. J. Chromatogr. A 1451, 127–134 (2016)
Wang, B., Huang, D.S., Jiang, C.J.: A new strategy for protein interface identification using manifold learning method. IEEE Trans. Nanobioscience 13, 118–123 (2014)
Chen, P., Hu, S., Zhang, J., Gao, X., Li, J., Xia, J., Wang, B.: A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction. IEEE/ACM Trans Comput Biol Bioinform 13, 901–912 (2016)
Ji, Z.W., Wang, B.: Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm. Biomed Res. Int. 2014, 12 (2014)
Zhao, Y., Zhang, J., Wang, B., Kim, S.H., Fang, A., Bogdanov, B., Zhou, Z., McClain, C., Zhang, X.: A method of calculating the second dimension retention index in comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. J. Chromatogr. A 1218, 2577–2583 (2011)
Chen, P., Liu, C., Burge, L., Li, J., Mohammad, M., Southerland, W., Gloster, C., Wang, B.: DomSVR: domain boundary prediction with support vector regression from sequence information alone. Amino Acid 39, 713–726 (2010)
Wang, B., Chen, P., Wang, P., Zhao, G., Zhang, X.: Radial basis function neural network ensemble for predicting protein-protein interaction sites in hetero complexes. Protein Pept Lett 17, 1111–1116 (2010)
Xia, S., Chen, P., Zhang, J., Li, X.P., Wang, B.: Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning. Neurocomputing 228, 11–18 (2017)
Hough, P.V.C.: Method and means for recognizing complex patterns (1962)
Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)
Chien, C.F., Cheng, Y.C., Lin, T.T.: Robust ellipse detection based on hierarchical image pyramid and Hough transform. J. Opt. Soc. Am. A: 28, 581–589 (2011)
Teng, A.Z., Kim, J.H., Kang, D.J.: Ellipse detection: a simple and precise method based on randomized Hough transform. Optical Eng. 51, 84 (2012)
Xu, L., Oja, E., Kultanen, P.: A new curve detection method: Randomized Hough transform (RHT). Pattern Recogn. Lett. 11, 331–338 (1990)
Yip, R.K.K., Tam, P.K.S., Leung, D.N.K.: Modification of hough transform for circles and ellipses detection using a 2-dimensional array. Pattern Recogn. 25, 1007–1022 (1992)
Kimme, C.: Finding circles by an array of accumulators. Commun. ACM 18, 120–122 (1975)
Atherton, T.J., Kerbyson, D.J.: Size invariant circle detection. Image Vis. Comput. 17, 795–803 (1999)
Li, X.L., Shao, Z.J., Qian, J.X.: An optimizing method based on autonomous animate: Fish swarm algorithm. Syst. Eng. Theor. Pract. 22, 32–38 (2002)
Gao, Y., Guan, L., Wang, T.: Optimal artificial fish swarm algorithm for the field calibration on marine navigation. Measurement 50, 297–304 (2014)
Yong, P., Tang, G.L., Xue, Z.C.: Optimal operation of cascade reservoirs based on improved artificial fish swarm algorithm. Syst. Eng. Theor. Pract. 31, 1118–1125 (2011)
Dong, N., Wu, C.H., Ip, W.H., Chen, Z.Q., Chan, C.Y., Yung, K.L.: An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection. Comput. Math. Appl. 64, 1886–1902 (2012)
Kelly, M., Levine, M.: Finding and Describing Objects in Complex Images: Advances in Image Understanding, pp. 209–225. IEEE Computer Society Press, Baco Raton (1997)
Acknowledgement
This work is supported National Natural Science Foundation of China under Grant Nos. 61472282 and 61672035, Natural Science Foundation of Anhui Province under Grant No.1508085MF129,and the Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui Uinversity of Technology, Minisitry of Education) under No. KF17-02.
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Wang, W., Lu, K., Hong, R., Chen, P., Zhang, J., Wang, B. (2017). A Machine Vision Method for Automatic Circular Parts Detection Based on Optimization Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_53
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