Detecting ellipses from images is a fundamental task in many computer vision applications. However, due to the complexity of real-world scenarios, it is still a challenge to detect ellipses accurately and efficiently. In this paper, we propose a novel method to tackle this problem based on the fast computation of convex hull and directed graph, which achieves promising results on both accuracy and efficiency. We use Depth-First-Search to extract branch-free curves after adaptive edge detection. Line segments are used to represent the curvature characteristic of the curves, followed by splitting at sharp corners and inflection points to attain smooth arcs. Then the convex hull is constructed, together with the distance, length, and direction constraints, to find co-elliptic arc pairs. Arcs and their connectivity are encoded into a sparse directed graph, and then ellipses are generated via a fast access of the adjacency list. Finally, salient ellipses are selected subject to strict verification and weighted clustering. Extensive experiments are conducted on eight real-world datasets (six publicly available and two built by ourselves), as well as five synthetic datasets. Our method achieves the overall highest F-measure with competitive speed compared to representative state-of-the-art methods.

从图像中检测椭圆是许多计算机视觉应用程序中的一项基本任务。然而，由于现实世界场景的复杂性，准确高效地检测椭圆仍然是一个挑战。在本文中，我们提出了一种基于凸包和有向图的快速计算来解决这个问题的新方法，这在准确性和效率方面都取得了可喜的成果。我们使用深度优先搜索在自适应边缘检测后提取无分支曲线。线段用于表示曲线的曲率特性，然后在尖角和拐点处分裂以获得平滑的弧线。然后构造凸包，连同距离、长度和方向约束，以找到共椭圆弧对。弧和它们的连通性被编码成一个稀疏有向图，然后通过快速访问邻接表生成椭圆。最后，经过严格验证和加权聚类选择显着椭圆。在八个真实世界的数据集（六个公开可用，两个由我们自己构建）以及五个合成数据集上进行了广泛的实验。