Efficient technique for circle detection using hypothesis filtering and Hough transform

A fast circle detection method using a variant of Hough-like technique is reported. The proposed technique is simple in implementation, efficient in computation and robust to noise. In general, to evaluate circle parameters for all possible point triplets in an edge image containing n points, C-n(3) enumerations of the points have to be examined. However, if specific relations of the circle points are sought, the required number of enumerations can be reduced. The authors propose one such scheme of detection with point triplets possessing right angle property and the required enumerations can be reduced to C-n(2). Moreover, a novel processing strategy known as hypothesis filtering is introduced. The strategy includes two hypothesis constraints termed as consistency checking with gradient angles and neighbouring points validation. Experimental results are demonstrated to reveal the performance of the method on detecting circles in both synthetic and real images. Since the proposed method adopts a right angle criterion for hypothesis, circles occluded or broken by more than one half may not be detected. Test results show that the limitation of the proposed method appears to be acceptable. When compared with established Hough transform techniques, the main strengths of the proposed detection method are its attractively low computational and memory complexities good accuracy of detection.