An improved method for circle detection

A split and merge method for detecting circular objects has been proposed in this paper. In the split stage, curve segmentation is performed by using a dominant point detection method. The segments are then classified as linear or nonlinear segments by the linearity criterion. In the merge stage, the geometric properties of circles are applied to estimate the centers and radii for nonlinear segments. The merge procedure is used to find if there is a better fitting error. The nonlinear segments are classified into several clusters. The circles will be identified if they have small fitting errors. The proposed method has been evaluated using both of the synthetic and real images. The experimental results show that the proposed method is efficient. It can detect both of the broken and occluded circular objects effectively. In addition, it is robust for the noisy images.