Denoising Algorithm Based on Neighborhood Expansion Clustering

Authors

Xinggang Li, School of Information, Yunnan Normal University, Kunming 650500, China;
Yaping Zhang, School of Information, Yunnan Normal University, Kunming 650500, China;
Yuwei Yang, School of Information, Yunnan Normal University, Kunming 650500, China;

Abstract

Abstract: Aimed at the three dimensional point clouds model with outliers and noises which is reconstructed based on 2D images, a new denoising algorithm based on neighborhood expansion clustering is proposed. The search for other neighboring points of each data point is conducted by using the Euclidean distance between data points and the transitive property of the neighborhood location relation. All points are processed for cluster partition, which detects and filters the outliers in the point clouds model. The concept of neighborhood expansion clustering and the fast search algorithm based on dynamic grids division are discussed. It solves the problem of detecting and filtering the outliers distributed in isolation or densely around the point clouds model, which improves the efficiency of traditional k-nearest neighbor algorithm to denoise the point clouds data. Simulation results show that the proposed algorithm can effectively filter the outliers distributed in isolation or densely around the point clouds model.

Recommended Citation

Li, Xinggang; Zhang, Yaping; and Yang, Yuwei (2020) "Denoising Algorithm Based on Neighborhood Expansion Clustering," Journal of System Simulation: Vol. 29: Iss. 11, Article 10.
DOI: 10.16182/j.issn1004731x.joss.201711010
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss11/10

First Page

2663

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201711010

Last Page

2670

CLC

TP391.72

Recommended Citation

Li Xinggang, Zhang Yaping, Yang Yuwei. Denoising Algorithm Based on Neighborhood Expansion Clustering[J]. Journal of System Simulation, 2017, 29(11): 2663-2670.

DOI

10.16182/j.issn1004731x.joss.201711010