A split and merge based ellipse detector

We present an ellipse detector that continually pools lower level information of the edge pixels together to achieve robust detection of the ellipses present in the image. In addition, the parameters of the detected ellipses are continually refined using a close loop system driven by Gestalt psychology. We highlight that we do not rely on the geometrical properties of the ellipses to detect the ellipses. In this aspect, our algorithm is well suited to detect partially occluded ellipses in the image. Experiments on real and synthetic images demonstrate the robustness of our algorithm in which both complete and incomplete ellipses can be detected. In particular, experimental results show that the mean detection accuracy of our algorithm surpasses 92% even with around 90% outliers in the images. This detection performance is superior to that achieved by the robust regression, least squares and the hough transform based ellipse detectors.