基于距离统计的有序纹理点云离群点检测
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引用本文:黄旺华?覮,王钦若.基于距离统计的有序纹理点云离群点检测[J].计算技术与自动化,2019,(1):139-144
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黄旺华?覮,王钦若 (广东工业大学 自动化学院广东 广州 510006) 
中文摘要:三维数据的离群点检测是纹理点云数据处理的重要内容之一,为了有效快速地检测离群点,根据纹理点云的有序结构特征,提出了基于距离统计的检测算法。首先在每个点到其K邻域中其他点距离的基础上计算出K邻域距离;然后根据有序点云中该距离符合正态分布的特点和正态分布3σ定理,将超出3倍方差范围的点认定为离群点。实验结果显示算法采用曼哈顿-最大距离进行检测,当K为4时可以更加快速准确地将有序点云中的离群点检测出来。由此得出,基于距离统计的算法可以有效地将离群点检测出来,同时成功地应用于纹理点云的离群点检测。
中文关键词:离群点检测  距离统计  K邻域距离  正态分布3σ定理  有序点云
 
Outlier Detection Based on Distance Statistics for Ordered Texture Point Cloud
Abstract:3D outlier detection is an important processing of texture point cloud,in order to effectively detect the outlier quickly,a outlier detection method based on distance statistics is proposed,according to the ordered structure characteristic of texture point cloud. K neighborhood distance of every point is calculated by the distances between the point and its every K neighborhood point firstly;and then as the K neighborhood distance of ordered point cloud follow the normal distribution and the normal distribution 3σ theorem,the point will be detected as outlier point if its K neighborhood distance is beyond 3σ range. The result of experiments show that the proposed method can more quickly and accurately to detect outlier,if Manhattan-Maximum distance is adapted and K is 4. The conclusion is that the outlier detection method based on distance statistics can effectively detect outliers,and is applied on texture point cloud successfully.
keywords:outlier detection  distance statistics  K neighborhood distance  normal distribution 3σ theorem  ordered point cloud
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