An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings
Joint Authors
Zhao, Jianghong
Dong, Yan
Ma, Siyu
Liu, Huajun
Wei, Shuangfeng
Zhang, Ruiju
Chen, Xi
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling.
Currently, it is also a challenging problem in point cloud processing.
There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation.
This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping.
The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius.
The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.
American Psychological Association (APA)
Zhao, Jianghong& Dong, Yan& Ma, Siyu& Liu, Huajun& Wei, Shuangfeng& Zhang, Ruiju…[et al.]. 2019. An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1194995
Modern Language Association (MLA)
Zhao, Jianghong…[et al.]. An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1194995
American Medical Association (AMA)
Zhao, Jianghong& Dong, Yan& Ma, Siyu& Liu, Huajun& Wei, Shuangfeng& Zhang, Ruiju…[et al.]. An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1194995
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194995