Abstract
Circle extraction is usually a pre-completed task used in different applications related to medical, robotics, biometrics image analysis among others. Randomized Hough Transform (RHT) determines the parameters of the circle by randomly obtaining three edge pixels, if they are not precisely located on the circumference. The detected circle will not perfectly match the ideal circle. At the same time, three random points are largely not on a circle, which leads to some invalid sampling and parameter accumulation. In this paper, an improved RHT combined with fitting subpixel circle detection algorithm is proposed. The improved RHT algorithm calculates and accumulates parameters by using 1 point obtained from random sampling and another two points obtained from horizontal and vertical search respectively. The algorithm introduces the edge map of the de-soliton point and small region, and improves the probability that three points belong to the same circle. Then, the set of edge pixels corresponding to the identified circle is fitted to reduce the bias effect caused by only using three edge pixels to calculate the circle parameters. In this way, the reliability of the fitting and the precision of the parameters are improved while removing the noise. Experimental tests were conducted for detection performance, accuracy of parameter estimation and noise robustness. Compared with other methods, the proposed method has strong anti-interference ability and high calculation accuracy.
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References
Akinlar C, Topal C (2013) EDCircles: a real-time circle detector with a false detection control. Pattern Recogn 46(3):725–740
Atherton TJ, Kerbyson DJ (1999) Size invariant circle detection. Image Vis Comput 17(11):795–803
Baker L, Mills S, Langlotz T, Rathbone C (2016) Power line detection using Hough transform and line tracing techniques. In 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ) p. 1–6. https://doi.org/10.1109/IVCNZ.2016.7804438
Cai J, Huang P, Chen L, Zhang B (2016) An efficient circle detector not relying on edge detection. Adv Space Res 57(11):2359–2375
Chia C-M, Huang K-Y, Chang E (2016) Hough transform used on the spot-centroiding algorithm for the shack–Hartmann wavefront sensor. Opt Eng 55(1):013105
Chung K-L, Huang YH, Shen SM, Krylov AS, Yurin DV, Semeikina EV (2012) Efficient sampling strategy and refinement strategy for randomized circle detection. Pattern Recogn 45(1):252–263
Cuevas E et al (2010) Circle detection using discrete differential evolution optimization. Pattern Anal Applic 14(1):93–107
Cuevas E et al (2011) Multi-circle detection on images using artificial bee colony (ABC) optimization. Soft Comput 16(2):281–296
Davies ER (1988) A modified Hough scheme for general circle location. Pattern Recogn Lett 7(1):37–43
De Marco T et al (2015) Randomized circle detection with isophotes curvature analysis. Pattern Recogn 48(2):411–421
Du G et al (2017) Classifying fragments of terracotta warriors using template-based partial matching. Multimed Tools Appl 77(15):19171–19191
Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15
Hoxie A, Ga M (2016) Median ellipse parameterization for robust measurement of fuel droplet size. Meas Sci Technol 27(2). (https://iopscience.iop.org/article/10.1088/0957-0233/27/2/025304)
HumbertoSossa EDDM-C (2011) Circle detection using electro-magnetism optimization. Information Sciences 182(1):40–55. https://doi.org/10.1016/j.ins.2010.12.024
Ioannou D, Huda W, Laine AF (1999) Circle recognition through a 2D Hough Transform and radius histogramming. Image Vis Comput 17(1):15–26
Kaur M (2017) A review of Hough transformation based lane detection techniques. Int J Adv Res Comput Sci 8(8):719–722
Kwon YC et al (2019) Multi-Cue-based circle detection and its application to robust extrinsic calibration of RGB-D cameras. Sensors (Basel) 19(7):1539
Li D, F.N., Tao X, et al (2017) Circle detection of short arc based on Randomized Hough Transform IEEE International Conference on Mechatronics & Automation. https://doi.org/10.1109/ICMA.2017.8015824
Li S, du Z, Yu H, Yi J (2019) A robust multi-circle detector based on horizontal and vertical search analysis fitting with tangent direction. Int J Pattern Recognit Artif Intell 33(04):1954013
Liang Q, Long J, Nan Y, Coppola G, Zou K, Zhang D, Sun W, 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 (2019) Angle aided circle detection based on randomized Hough transform and its application in welding spots detection. Math Biosci Eng 16(3):1244–1257
Lopez-Martinez A, Cuevas FJ (2018) Automatic circle detection on images using the teaching learning based optimization algorithm and gradient analysis. Appl Intell 49(5):2001–2016
Luo J, Zou H, Chen X, Gao H (2020) A fast circle detection method based on a tri-class Thresholding for high detail FPC images. IEEE Trans Instrum Meas 69(4):1327–1335
Mukhopadhyay P, Chaudhuri BB (2015) A survey of Hough transform. Pattern Recogn 48(3):993–1010
Nausheen N, Seal A, Khanna P, Halder S (2018) A FPGA based implementation of Sobel edge detection. Microprocess Microsyst 56(1):84–91
Thomas SM, Chan Y-T (1989) A simple approach for the estimation of circular arc center and its radius. Computer Vision, Graphics, and Image Processing 45(3):362–370
Torrente M-L, Biasotti S, Falcidieno B (2018) Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms. Pattern Recogn 73(1):111–130
Wang G et al (2019) Vision technique for deflection measurements based on laser positioning. Eur J Environ Civ Eng:1–23
Wang H et al (2020) Improving artificial Bee colony algorithm using a new neighborhood selection mechanism. Information Sciences 527(22):227–240. https://doi.org/10.1016/j.ins.2020.03.064
West PLRGAW (1995) Nonparametric segmentation of curves into various representations. IEEE Transactions on Pattern Analysis & Machine Intelligence 17(12):1140–1153
Xiao F, Huang K, Qiu Y, Shen H (2018) Accurate iris center localization method using facial landmark, snakuscule, circle fitting and binary connected component. Multimed Tools Appl 77(19):25333–25353
Xu J-k (1993) Randomized hough transform (RHT) basic mechanisms, algorithms, and computational complexities. CVGIP: Image understanding 57(2):131–154
Yao Z, Yi W (2016) Curvature aided Hough transform for circle detection. Expert Syst Appl 51(9):26–33
Yuen HK, Princen J, Illingworth J, Kittler J (1990) Comparative study of Hough Transform methods for circle finding. Image and vision computing 8, 71(1):–77
Zhu J et al (2018) Laser spot center detection and comparison test. Photonic Sensors 9(1):49–52
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Wang, G. A sub-pixel circle detection algorithm combined with improved RHT and fitting. Multimed Tools Appl 79, 29825–29843 (2020). https://doi.org/10.1007/s11042-020-09514-0
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DOI: https://doi.org/10.1007/s11042-020-09514-0