Ellipse Detection Based on Contrast-Guided Arc Enhancement

Zikai Wang, Baojiang Zhong, Kai-Kuang Ma

Abstract: A common practice of existing ellipse detection methods is to produce an ellipse by grouping elliptic arcs. Thus, the quality (i.e., continuity) of elliptic arcs will have a significant impact on the performance of ellipse detection. In this paper, a novel contrast-guided ellipse detection method is proposed for generating elliptic arcs with improved quality. After applying an edge detector to the input image, all the nearby pixels along each edge are evaluated for possibly re-classifying them to the opposite class; that is, non-edge pixels might be re-classified as edge pixels, and vice versa. With the proposed contrast-guided arc enhancement, the produced elliptic arcs tend to have better quality, which is essential to detect ellipses more accurately. To further assure the detected ellipses, a novel ellipse validation process is developed and used to discard those ellipses with low confidence. Extensive experimental results have shown that the proposed method can deliver superior performance in nearly all test cases.