散乱点云近离群点识别算法  

Near outlier detection of scattered point cloud

作　　者：赵京东[1] 杨凤华[1] 刘爱晶[1] 

机构地区：[1]曲阜师范大学数学科学学院,山东曲阜273165

出　　处：《计算机应用》2015年第4期1089-1092,1128,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61104136)

摘　　要：针对原始曲面变化度的局部离群系数(SVLOF)无法有效滤除三维实体的棱边或棱角处的离群点问题,提出了一种散乱点云近离群点的滤除算法。该算法首先将SVLOF定义在类k邻域上,并将SVLOF的定义内容进行了扩展,使其既能滤除平滑曲面上的离群点,又能滤除三维实体的棱边或棱角点处的离群点,同时仍然保留SVLOF原有的足够宽泛的阈值选取空间。仿真数据和实际数据的实验结果均表明,在效率基本保持不变的情况下,所提算法能比原始SVLOF算法更有效地检测出距离主体点云近的离群点。Concerning that the original Surface Variation based Local Outlier Factor（ SVLOF） cannot filter out the outliers on edges or corners of three-dimensional solid, a new near outlier detection algorithm of scattered point cloud was proposed. This algorithm firstly defined SVLOF on the k neighborhood-like region, and expanded the definition of SVLOF.The expanded SVLOF can not only filter outliers on smooth surface but also filter outliers on edges or corners of threedimensional solid. At the same time, it still retains the space of threshold value enough of original SVLOF. The experimental results of the simulation data and measured data show that the new algorithm can detect the near outliers of scattered point cloud effectively without changing the efficiency obviously.

关 键 词：散乱点云 离群点 局部离群系数 基于曲面变化度的局部离群系数 

分 类 号：TP391.72[自动化与计算机技术—计算机应用技术]