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Over the years many ellipse detection algorithms spring up and are studiedbroadly, while the critical issue of detecting ellipses accurately andefficiently in real-world images remains a challenge. In this paper, we proposea valuable industry-oriented ellipse detector by arc-support line segments,which simultaneously reaches high detection accuracy and efficiency. Tosimplify the complicated curves in an image while retaining the generalproperties including convexity and polarity, the arc-support line segments areextracted, which grounds the successful detection of ellipses. The arc-supportgroups are formed by iteratively and robustly linking the arc-support linesegments that latently belong to a common ellipse. Afterward, two complementaryapproaches, namely, locally selecting the arc-support group with highersaliency and globally searching all the valid paired groups, are adopted to fitthe initial ellipses in a fast way. Then, the ellipse candidate set can beformulated by hierarchical clustering of 5D parameter space of initialellipses. Finally, the salient ellipse candidates are selected and refined asdetections subject to the stringent and effective verification. Extensiveexperiments on three public datasets are implemented and our method achievesthe best F-measure scores compared to the state-of-the-art methods. The sourcecode is available athttps://github.com/AlanLuSun/High-quality-ellipse-detection.

Arc-support Line Segments Revisited: An Efficient and High-quality Ellipse Detection