Lu Changsheng, Xia Siyu, Shao Ming, Fu Yun
IEEE Trans Image Process. 2019 Aug 15. doi: 10.1109/TIP.2019.2934352.
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.
多年来，许多椭圆检测算法如雨后春笋般涌现并得到广泛研究，然而在真实世界图像中准确高效地检测椭圆这一关键问题仍然是一项挑战。在本文中，我们提出了一种基于弧支撑线段的、面向行业的有价值的椭圆检测器，它能同时实现高检测精度和效率。为了在保留包括凸性和极性等一般属性的同时简化图像中的复杂曲线，提取了弧支撑线段，这为椭圆的成功检测奠定了基础。通过迭代且稳健地连接潜在属于同一个椭圆的弧支撑线段来形成弧支撑组。之后，采用两种互补的方法，即局部选择具有更高显著性的弧支撑组和全局搜索所有有效的配对组，以快速拟合初始椭圆。然后，通过对初始椭圆的5D参数空间进行层次聚类来形成椭圆候选集。最后，在经过严格有效的验证后，选择并细化显著的椭圆候选作为检测结果。在三个公开数据集上进行了大量实验，与现有方法相比，我们的方法取得了最佳的F值分数。源代码可在https://github.com/AlanLuSun/High-quality-ellipse-detection获取。
