Efficient sampling strategy and refinement strategy for randomized circle detection

Circle detection is fundamental in pattern recognition and computer vision. The randomized approach has received much attention for its computational benefit when compared with the Hough transform. In this paper, a multiple-evidence-based sampling strategy is proposed to speed up the randomized approach. Next, an efficient refinement strategy is proposed to improve the accuracy. Based on different kinds of ten test images, experimental results demonstrate the computation-saving and accuracy effects when plugging the proposed strategies into three existing circle detection methods.

► We first present a multiple-evidence-based sampling strategy to speed up the randomized approach.
► We also present a linear-time refinement strategy to improve the accuracy.
► Experimental results demonstrate the computation-saving and accuracy effects of the proposed two strategies.