A sub-pixel circle detection algorithm combined with improved RHT and fitting

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.