论文标题
从具有应用到图像云中图像和圆柱检测的圆形目标提取的点，对非重叠的椭圆的强核检测
Robust Detection of Non-overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds
论文作者
论文摘要
该手稿提供了一系列新方法，用于从边缘点自动检测非重叠的椭圆。这些方法在以下方法中介绍了新的发展：（i）在异常值的存在下，强大的基于蒙特卡洛的椭圆拟合到二维（2D）点； （ii）从2D边缘点检测非重叠椭圆； （iii）从3D点云中提取圆柱体。通过设计四组原始实验的设计，使用模拟和现实数据集将所提出的方法与已建立的最新方法（使用模拟和现实数据集进行了比较）。发现所提出的强大椭圆检测优于四种可靠的鲁棒方法，包括在模拟和现实世界中的流行最小平方中位数。使用Fornaciari，Patraucean和Panagiotakis的方法获得的F测量方法分别为42.4％，65.6％和59.2％，检测到实际图像的非重叠椭圆的拟议过程在实际图像上达到了99.3％。提出的圆柱提取方法在实验室和工业建筑工地条件下获得的两个现实世界云中确定了所有可检测的机械管道。这项调查的结果表明，提出的方法可以从点云中自动提取圆形靶标。
This manuscript provides a collection of new methods for the automated detection of non-overlapping ellipses from edge points. The methods introduce new developments in: (i) robust Monte Carlo-based ellipse fitting to 2-dimensional (2D) points in the presence of outliers; (ii) detection of non-overlapping ellipse from 2D edge points; and (iii) extraction of cylinder from 3D point clouds. The proposed methods were thoroughly compared with established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3% on real images, compared to F-measures of 42.4%, 65.6%, and 59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively. The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds, obtained under laboratory, and industrial construction site conditions. The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.
