Research article

Fast outlier removing method for point cloud of microscopic 3D measurement based on social circle

  • Received: 19 August 2020 Accepted: 11 November 2020 Published: 13 November 2020
  • 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.

    Citation: Haihua Cui, Qianjin Wang, Dengfeng Dong, Hao Wei, Yihua Zhang. Fast outlier removing method for point cloud of microscopic 3D measurement based on social circle[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 8138-8151. doi: 10.3934/mbe.2020413

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  • 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.
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    [1] M. Berger, A. Tagliasacchi, L. Seversky, P. Alliez, J. Levine, A. Sharf, et al., State of the art in surface reconstruction from point clouds, Eurographics 2014 - State of the Art Reports, 1 (2014), 161-185.
    [2] R. Leach, Optical measurement of surface topography, Springer, Berlin, 2011.
    [3] S. Sotoodeh, Hierarchical clustered outlier detection in laser scanner point clouds, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 36 (2007), 383-388.
    [4] J. Yu, M. Wei, J. Qin, Feature-preserving mesh denoising via normal guided quadric error metrics, Opt. Lasers Eng., 62 (2014), 57-68.
    [5] X. F. Han, J. Jin, M. J. Wang, W. Wei, L. Gao, L. Xiao, A review of algorithms for filtering the 3D point cloud, Signal Process. Image Commun., 57 (2017), 103-112.
    [6] Y. Wang, H. Y. Feng, Outlier detection for scanned point clouds using majority voting, Comput.-Aided Des., 62 (2015), 31-43.
    [7] A. Belyaev, Y. Ohtake, A comparison of mesh smoothing methods, Israel-Korea Bi-national conference on geometric modeling and computer graphics, 2 (2003), 83-87.
    [8] T. Jones, F. Durand, M. Desbrun, Non-iterative, feature-preserving mesh smoothing, ACM SIGGRAPH 2003 Papers, 22 (2003), 943-949.
    [9] B. Skinner, T. Vidal-Calleja, J. V. Miro, F. De Bruijn, R. Falque, 3D point cloud upsampling for accurate reconstruction of dense 2.5 D thickness maps, Australasian Conference on Robotics and Automation, ACRA, 2014.
    [10] V. Morell, S. Orts, M. Cazorla, J. Garcia-Rodriguez, Geometric 3D point cloud compression, Pattern Recognit. Lett., 50 (2014), 55-62.
    [11] R. Rusu, Z. Marton, N. Blodow, M. Dolha, M. Beetz, Towards 3D point cloud based object maps for household environments, Rob. Auton. Syst., 56 (2008), 927-941.
    [12] S. Fleishman, I. Drori, D. Cohen-Or, Bilateral mesh denoising, ACM SIGGRAPH 2003 Papers. 22 (2003), 950-953.
    [13] T. Pachur, J. Rieskamp, R. Hertwig, The social circle heuristic: Fast and frugal decisions based on small samples, Proceedings of the Annual Meeting of the Cognitive Science Society, 26 (2004), 1077-1082.
    [14] G. Li, Z. Pan, B. Xiao, L. Huang, Community discovery and importance analysis in social network, Intell. Data Analysis, 18 (2014), 495-510.
    [15] S. K. Nayar, Y. Nakagawa, Shape from focus, IEEE Trans. Pattern Anal. Mach. Intell., 16 (1994), 824-831.
    [16] J. He, R. Zhou, Z. Hong, Modified fast climbing search auto-focus algorithm with adaptive step size searching technique for digital camera, IEEE Trans. Consum. Electron., 49 (2003), 257-262.
    [17] J. Leskovec, J. Mcauley, Learning to discover social circles in ego networks, Adv. Neural Inf. Process. Syst., 25 (2012), 539-547.
    [18] M. Wang, W. Zuo, Y. Wang, An improved density peaks-based clustering method for social circle discovery in social networks, Neurocomputing, 179 (2012), 219-227.
    [19] S. Wang, F. Wang, Y. Chen, C. Liu, Z. Li, X. Zhang, Exploiting social circle broadness for influential spreaders identification in social networks. World Wide Web, 18 (2015), 681-705.

    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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