Bolt 3D Point Cloud Segmentation and Measurement Based on DBSCAN Clustering

Lu, Zhengdong; Zhu, Zhangqi; Bi, Jianghai; Xiong, Kuixiang; Wang, Jijun; Lu, Chenbin; Bao, Zhejing; Yan, Wenjun

doi:10.1109/cac53003.2021.9728702

Cited by 5 publications

(2 citation statements)

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“…For an explicit description of the region-growing algorithm in the context of railways, the interested reader is referred to Cserép, Demján, Mayer, et al [37, Algorithm 1 and Algorithm 2]. Other examples of region-growing algorithms can be found in the work of Arastounia [23], Chbeir, Ponciano, Prudhomme, et al [32], Zhang, Wang, Yang, et al [22], Lu, Zhu, Bi, et al [80], and Zou, Fan, Qian, et al [70].…”

Section: ) Region-growing Methodsmentioning

confidence: 99%

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

Dekker,

Ton,

Meijer

et al. 2023

IEEE Access

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Section: ) Region-growing Methodsmentioning

confidence: 99%

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

Dekker,

Ton,

Meijer

et al. 2023

IEEE Access

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“…In addition, sparsity-related methods and deep learning-based methods have been used for point clouds’ quality enhancement [ 24 ]. Some sparsity-related methods, such as K-mean [ 25 ] and density-based spatial clustering of applications with noise (DBSCAN) algorithm [ 26 ], have been used to cluster firstly to remove outliers in the point clouds. However, these technologies could not adequately remove a sufficient number of outlier points.…”

Section: Introductionmentioning

confidence: 99%

Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Liang,

Liu,

Yang

et al. 2024

Sensors

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Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.

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