Open AccessFast outlier removing method for point cloud of microscopic 3D measurement based on social circle
Author(s) -
Haihua Cui,
Qianjin Wang,
Dengfeng Dong,
Hao Wei,
Yihua Zhang
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020413
Subject(s) - outlier , point cloud , computer science , algorithm , noise (video) , voting , artificial intelligence , point (geometry) , gaussian function , computer vision , gaussian , mathematics , image (mathematics) , geometry , physics , quantum mechanics , politics , political science , law
Measurement outliers are easily caused by illumination, surface texture, human factors and so on during the process of microscopic topography measurement. These numerous cloud point noise will heavily affect instrument measurement accuracy and surface reconstruction quality. We propose a quick and accurate method for removing outliers based on social circle algorithm. First, the gaussian kernel function is used to calculate the voting value to determine the social circle's initial point, and then select the appropriate social circle radius and search window based on the initial point, and finally expand the social circle through an iterative method. Points which are not in the social circle can be considered as outliers and filtered out. The experimental results show the good performance of the algorithm with comparison to the existing filtering methods. The developed method has great potential in microscopic topography reconstruction, fitting and other point cloud processing tasks.