  Over the years many ellipse detection algorithms spring up and are studied
broadly, while the critical issue of detecting ellipses accurately and
efficiently in real-world images remains a challenge. In this paper, we propose
a valuable industry-oriented ellipse detector by arc-support line segments,
which simultaneously reaches high detection accuracy and efficiency. To
simplify the complicated curves in an image while retaining the general
properties including convexity and polarity, the arc-support line segments are
extracted, which grounds the successful detection of ellipses. The arc-support
groups are formed by iteratively and robustly linking the arc-support line
segments that latently belong to a common ellipse. Afterward, two complementary
approaches, namely, locally selecting the arc-support group with higher
saliency and globally searching all the valid paired groups, are adopted to fit
the initial ellipses in a fast way. Then, the ellipse candidate set can be
formulated by hierarchical clustering of 5D parameter space of initial
ellipses. Finally, the salient ellipse candidates are selected and refined as
detections subject to the stringent and effective verification. Extensive
experiments on three public datasets are implemented and our method achieves
the best F-measure scores compared to the state-of-the-art methods. The source
code is available at
https://github.com/AlanLuSun/High-quality-ellipse-detection.
