Ancient architecture point cloud data segmentation based on modified fuzzy C-means clustering algorithm

2008 ◽  
Author(s):  
Jianghong Zhao ◽  
Deren Li ◽  
Yanmin Wang
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Fuzzy C Means ◽  
Data Segmentation ◽  
Fuzzy C Means Clustering

scholarly journals RESEARCH ON EVALUATION METHOD OF AIRBORNE LIDAR POSITIONING ACCURACY

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 825-830
Author(s):  
Y. Zhang ◽  
G. Q. Zhou ◽  
S. H. Tang ◽  
P. W. Xing ◽  
C. C. Huang
Keyword(s):  
Point Cloud ◽  
Evaluation Method ◽  
Model Fitting ◽  
Airborne Lidar ◽  
Point Cloud Data ◽  
Lidar System ◽  
Actual Measurement ◽  
Cloud Data ◽  
Edge Points ◽  
Fuzzy C Means Clustering

Abstract. Due to the semi-random characteristics of ground points collected by airborne LIDAR system, it is difficult to control the laser pin points to fall on the control points with known coordinates in actual measurement, so the accuracy can not be evaluated by directly comparing coordinate data. In this paper, based on the target plate designed by air-to-ground, the fuzzy c-means clustering analysis algorithm is proposed to extract the point cloud data on the target according to the different echo intensity data of laser on different ground objects. The center point coordinates of the target were fitted by the point cloud data on the target using the method of circumscribed circle of edge points, so as to realize the plane precision and elevation accuracy of airborne LIDAR system of evaluation. The results show that the model fitting method can quickly and effectively evaluate the accuracy of airborne LIDAR, and the method is simple and feasible.

scholarly journals An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 172 ◽  
Author(s):  
Chunxiao Wang ◽  
Min Ji ◽  
Jian Wang ◽  
Wei Wen ◽  
Ting Li ◽  
...  
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Estimation Method ◽  
Point Clouds ◽  
Maximum Distance ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Segmentation ◽  
Different Types ◽  
Cloud Data Processing

Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the fitting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.

scholarly journals Point Cloud Data Segmentation Using RANSAC and Localization

2019 ◽  
Vol 705 ◽  
pp. 012004
Author(s):  
S. N. Mohd Isa ◽  
S. A. Abdul Shukor ◽  
N. A. Rahim ◽  
I. Maarof ◽  
Z. R. Yahya ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Segmentation

scholarly journals Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

2021 ◽  
Vol 13 (1) ◽  
pp. 705-716
Author(s):  
Qiuji Chen ◽  
Xin Wang ◽  
Mengru Hang ◽  
Jiye Li
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Tree Height ◽  
Mining Area ◽  
Research Area ◽  
Airborne Lidar ◽  
Shaanxi Province ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data

Abstract The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

scholarly journals Graphic: Graph-Based Hierarchical Clustering for Single-Molecule Localization Microscopy

2020 ◽  
Author(s):  
Mehrsa Pourya ◽  
Shayan Aziznejad ◽  
Michael Unser ◽  
Daniel Sage
Keyword(s):  
Single Molecule ◽  
Point Cloud ◽  
Clustering Algorithm ◽  
Weighted Graph ◽  
Point Cloud Data ◽  
Localization Microscopy ◽  
Cloud Data ◽  
Multiscale Clustering ◽  
Novel Method ◽  
Single Molecule Localization Microscopy

ABSTRACTWe propose a novel method for the clustering of point-cloud data that originate from single-molecule localization microscopy (SMLM). Our scheme has the ability to infer a hierarchical structure from the data. It takes a particular relevance when quantitatively analyzing the biological particles of interest at different scales. It assumes a prior neither on the shape of particles nor on the background noise. Our multiscale clustering pipeline is built upon graph theory. At each scale, we first construct a weighted graph that represents the SMLM data. Next, we find clusters using spectral clustering. We then use the output of this clustering algorithm to build the graph in the next scale; in this way, we ensure consistency over different scales. We illustrate our method with examples that highlight some of its important properties.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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