Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Exploiting a Semi-Automatic Point Cloud Segmentation Method to Improve the Quality of Rock-Mass Characterization. The Cima Grappa Conservative Restoration Case Study

2021 ◽  
Vol 10 (5) ◽  
pp. 276
Author(s):  
Francesco Mugnai ◽  
Paolo Farina ◽  
Grazia Tucci
Keyword(s):  
Rock Mass ◽  
Point Cloud ◽  
Laser Scanning ◽  
Automatic Segmentation ◽  
Point Clouds ◽  
Segmentation Method ◽  
Rock Mass Characterization ◽  
Point Cloud Segmentation ◽  
Conservative Restoration ◽  
The Military

This paper presents results from applying semi-automatic point cloud segmentation methods in the underground tunnels within the Military Shrine’s conservative restoration project in Cima Grappa (Italy). The studied area, which has a predominant underground development distributed in a network of tunnels, is characterized by diffuse rock collapsing. In such a context, carrying out surveys and other technical operations are dangerous activities. Considering safety restrictions and unreachable impervious tunnels, having approached the study area with the scan-line survey technique resulted in only partial rock mass characterization. Hence, the geo-mechanical dataset was integrated, applying a semi-automatic segmentation method to the point clouds acquired through terrestrial laser scanning (TLS). The combined approach allowed for remote performance of detailed rock mass characterization, even remotely, in a short time and with a limited operators presence on site. Moreover, it permitted extending assessing tunnels’ stability and state of conservation to the inaccessible areas.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

scholarly journals An MFF-SLIC Hybrid Superpixel Segmentation Method with Multi-Source RS Data for Rock Surface Extraction

2019 ◽  
Vol 9 (5) ◽  
pp. 906 ◽  
Author(s):  
Xuefeng Yi ◽  
Rongchun Zhang ◽  
Hao Li ◽  
Yuanyuan Chen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Distance Measure ◽  
Digital Images ◽  
Measurement Techniques ◽  
Rock Surface ◽  
Segmentation Method ◽  
Surface Extraction ◽  
Superpixel Segmentation ◽  
Relationship Construction

Multi-Source RS data integration is a crucial technology for rock surface extraction in geology. Both Terrestrial laser scanning (TLS) and Photogrammetry are primary non-contact active measurement techniques. In order to extract comprehensive and accurate rock surface information by the integration of TLS point cloud and digital images, the segmentation based on the integrated results generated by registration is the crux. This paper presents a Multi-Features Fusion for Simple Linear Iterative Clustering (MFF-SLIC) hybrid superpixel segmentation algorithm to extract the rock surface accurately. The MFF-SLIC algorithm mainly includes three contents: (1) Mapping relationship construction for TLS point cloud and digital images; (2) Distance measure model establishment with multi-features for initial superpixel segmentation; (3) Hierarchical and optimized clustering for superpixels. The proposed method was verified with the columnar basalt data, which is acquired in Guabushan Geopark in China. The results demonstrate that the segmentation method could be used for rock surface extraction with high precision and efficiency, the result of which would be prepared for further geological statistics and analysis.

scholarly journals An Ore Image Segmentation Method Based on RDU-Net Model

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4979
Author(s):  
Dong Xiao ◽  
Xiwen Liu ◽  
Ba Tuan Le ◽  
Zhiwen Ji ◽  
Xiaoyu Sun
Keyword(s):  
Image Segmentation ◽  
Production Efficiency ◽  
Fragment Size ◽  
Conveyor Belt ◽  
Segmentation Method ◽  
Residual Structure ◽  
Production Safety ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Main Index

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

scholarly journals Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra

2019 ◽  
Vol 9 (12) ◽  
pp. 335 ◽  
Author(s):  
Gašper Zupan ◽  
Dušan Šuput ◽  
Zvezdan Pirtošek ◽  
Andrej Vovk
Keyword(s):  
Substantia Nigra ◽  
Sensitivity And Specificity ◽  
Roc Analysis ◽  
Automatic Segmentation ◽  
High Sensitivity ◽  
Segmentation Method ◽  
Surface Sensitivity ◽  
Volume Sensitivity ◽  
Segmentation Methods ◽  
The Difference

In Parkinson’s disease (PD), there is a reduction of neuromelanin (NM) in the substantia nigra (SN). Manual quantification of the NM volume in the SN is unpractical and time-consuming; therefore, we aimed to quantify NM in the SN with a novel semi-automatic segmentation method. Twenty patients with PD and twelve healthy subjects (HC) were included in this study. T1-weighted spectral pre-saturation with inversion recovery (SPIR) images were acquired on a 3T scanner. Manual and semi-automatic atlas-free local statistics signature-based segmentations measured the surface and volume of SN, respectively. Midbrain volume (MV) was calculated to normalize the data. Receiver operating characteristic (ROC) analysis was performed to determine the sensitivity and specificity of both methods. PD patients had significantly lower SN mean surface (37.7 ± 8.0 vs. 56.9 ± 6.6 mm2) and volume (235.1 ± 45.4 vs. 382.9 ± 100.5 mm3) than HC. After normalization with MV, the difference remained significant. For surface, sensitivity and specificity were 91.7 and 95 percent, respectively. For volume, sensitivity and specificity were 91.7 and 90 percent, respectively. Manual and semi-automatic segmentation methods of the SN reliably distinguished between PD patients and HC. ROC analysis shows the high sensitivity and specificity of both methods.

Color Image Segmentation Based on Secondary Watershed and GrowCut Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 4032-4037
Author(s):  
Jian Mei Chen ◽  
Hai Ying Lu
Keyword(s):  
Color Image ◽  
Automatic Segmentation ◽  
User Interaction ◽  
Input Image ◽  
Color Image Segmentation ◽  
Superior Performance ◽  
Segmentation Method ◽  
Interactive Algorithm ◽  
Segmentation Accuracy ◽  
Label Algorithm

GrowCut algorithm is not only an interactive algorithm on the basis of cell automata, but also a multi-label algorithm based on seeds point. Aiming at the GrowCut algorithm usually asks users to partition foreground and background manually and mark a lot more initial seeds. This paper presents an automatic object segmentation method which combining secondary watershed and GrowCut algorithm, here in the following paper refers it to as SWGC algorithm. It firstly using the twice used watershed algorithm to partition the input image, the segmented regions are labeled using Mahalanobis distance, and merged according to the image color and space information, thereafter applying the GrowCut algorithm to perform globally optimized segmentation. The main contribution focuses on performing automatic segmentation which consist of obtain the foreground and background region and generate the seed template of GrowCut algorithm automatically. Thus not only leave out the constraints of user interaction operation, but also avoid the subjectivity and uncertainty. The proposed method reduces the runtime significantly as well as improves the segmentation accuracy and robustness of GrowCut algorithm. Experimental results show SWGC algorithm has superior performance compared to the other related methods.

scholarly journals Influence of Control Points Configuration on the Mobile Laser Scanning Accuracy

2021 ◽  
Vol 906 (1) ◽  
pp. 012091
Author(s):  
Petr Kalvoda ◽  
Jakub Nosek ◽  
Petra Kalvodova
Keyword(s):  
Reference Point ◽  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Data Sets ◽  
Control Points ◽  
Mobile Mapping ◽  
Data Set ◽  
Accuracy Test ◽  
Data Subset

Abstract Mobile mapping systems (MMS) are becoming widely used in standard geodetic tasks more commonly in the last years. The paper is focused on the influence of control points (CPs) number and configuration on mobile laser scanning accuracy. The mobile laser scanning (MLS) data was acquired by MMS RIEGL VMX-450. The resulting point cloud was compared with two different reference data sets. The first reference data set consisted of a high-accuracy test point field (TPF) measured by a Trimble R8s GNSS system and a Trimble S8 HP total station. The second reference data set was a point cloud from terrestrial laser scanning (TLS) using two Faro Focus3D X 130 laser scanners. The coordinates of both reference data sets were determined with significantly higher accuracy than the coordinates of the tested MLS point cloud. The accuracy testing is based on coordinate differences between the reference data set and the tested MLS point cloud. There is a minimum number of 6–7 CPs in our scanned area (based on MLS trajectory length) to achieve the declared relative accuracy of trajectory positioning according to the RIEGL datasheet. We tested two types of ground control point (GCP) configurations for 7 GCPs, using TPF reference data. The first type is a trajectory-based CPs configuration, and the second is a geometry-based CPs configuration. The accuracy differences of the MLS point clouds with trajectory-based CPs configuration and geometry-based CPs configuration are not statistically significant. From a practical perspective, a geometry-based CPs configuration is more advantageous in the nonlinear type of urban area such as our one. The following analyzes are performed on geometry-based CPs configuration variants. We tested the influence of changing the location of two CPs from ground to roof. The effect of the vertical configuration of the CPs on the accuracy of the tested MLS point cloud has not been demonstrated. The effect of the number of control points on the accuracy of the MLS point cloud was also tested. In the overall statistics using TPF, the accuracy increases significantly with increasing the number of GCPs up to 6. This number corresponds to a requirement of the manufacturer. Although further increasing the number of CPs does not significantly increase the global accuracy, local accuracy improves with increasing the number of CPs up to 10 (average spacing 50 m) according to the comparison with the TLS reference point cloud. The accuracy test of the MLS point cloud was divided into the horizontal accuracy test on the façade data subset and the vertical accuracy test on the road data subset using the TLS reference point cloud. The results of this paper can help improve the efficiency and accuracy of the mobile mapping process in geodetic praxis.

scholarly journals An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jianghong Zhao ◽  
Yan Dong ◽  
Siyu Ma ◽  
Huajun Liu ◽  
Shuangfeng Wei ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Dbscan Algorithm ◽  
Density Clustering ◽  
Low Efficiency ◽  
Definition Of

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.

Research on CCD Infrared Image Threshold Segmentation

2012 ◽  
Vol 220-223 ◽  
pp. 1292-1297
Author(s):  
Xing Ma ◽  
Jun Li Han ◽  
Chang Shun Liu
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Infrared Image ◽  
Experimental System ◽  
Segmentation Method ◽  
Gray Scale ◽  
Image Detection ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Characteristics Analysis

In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image.

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