Ellipse Detection Based On Structure-Preserving Anisotropic Edge Extraction

Yang Su, Baojiang Zhong, Zikai Wang, Kai-Kuang Ma

Abstract: Existing methods for ellipse detection popularly adopt the edge-linking strategy—i.e., first combining the elliptical arcs extracted from an edge map into groups and then fitting each group of arcs to an ellipse. However, such methods generally use the Canny operator to extract edges, which tends to corrupt salient elliptical shapes in texture regions, thus preventing the effective detection of ellipses. To overcome the difficulty, we propose a novel ellipse detector based on our developed structure-preserving anisotropic edge extraction (SPAEE) approach, which can remove redundant textures and preserve continuous structural edges, thus improving the performance of ellipse detection. In addition, an adaptive validation strategy is proposed to further enhance the detection quality. To our best knowledge, this is the first attempt to detect ellipses with an anisotropic edge extraction process for preserving structural edges. Experimental results have shown that the mean F-score on five benchmark datasets has increased from 0.61 to 0.67 (about 10% performance gain).