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Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it diffi...

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Detalles Bibliográficos
Autores principales: Kim, Jongwon, Cho, Jeongho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767241/
https://www.ncbi.nlm.nih.gov/pubmed/31547226
http://dx.doi.org/10.3390/s19183926
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author Kim, Jongwon
Cho, Jeongho
author_facet Kim, Jongwon
Cho, Jeongho
author_sort Kim, Jongwon
collection PubMed
description In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.
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spelling pubmed-67672412019-10-02 Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds Kim, Jongwon Cho, Jeongho Sensors (Basel) Article In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining. MDPI 2019-09-12 /pmc/articles/PMC6767241/ /pubmed/31547226 http://dx.doi.org/10.3390/s19183926 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Jongwon
Cho, Jeongho
Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds
title Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds
title_full Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds
title_fullStr Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds
title_full_unstemmed Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds
title_short Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds
title_sort delaunay triangulation-based spatial clustering technique for enhanced adjacent boundary detection and segmentation of lidar 3d point clouds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767241/
https://www.ncbi.nlm.nih.gov/pubmed/31547226
http://dx.doi.org/10.3390/s19183926
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