Cylinder Detection From RGBD Data Based on Radius Estimation Using Number of Measurement Points

2021 ◽  
Author(s):  
Tomoki Kawagoshi ◽  
Kimitoshi Yamazaki
Keyword(s):  
Cylinder Detection

scholarly journals Connectivity-based cylinder detection in unorganized point clouds

2020 ◽  
Vol 100 ◽  
pp. 107161 ◽  
Author(s):  
Abner M.C. Araújo ◽  
Manuel M. Oliveira
Keyword(s):  
Point Clouds ◽  
Cylinder Detection

scholarly journals Cylinder Detection in Large-Scale Point Cloud of Pipeline Plant

2013 ◽  
Vol 19 (10) ◽  
pp. 1700-1707 ◽  
Author(s):  
Yong-Jin Liu ◽  
Jun-Bin Zhang ◽  
Ji-Chun Hou ◽  
Ji-Cheng Ren ◽  
Wei-Qing Tang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Scale Point ◽  
Cylinder Detection

Characteristics Analysis of Acoustic Emission Signals from Tensile Process of High Pressure Gas Cylinder Materials

2013 ◽  
Vol 546 ◽  
pp. 84-88
Author(s):  
Qiu Juan Lv ◽  
Min Chen ◽  
Yan Jiao Li ◽  
Zhi Qing Guo ◽  
Chang Jiang Liu
Keyword(s):  
High Pressure ◽  
Acoustic Emission ◽  
Theory Analysis ◽  
Gas Cylinder ◽  
Tensile Process ◽  
Characteristics Analysis ◽  
Tensile Experiment ◽  
Ae Signals ◽  
Cylinder Detection

Conducting tensile experiment with high pressure gas cylinder materials to analyze its acoustic emission (AE) signals, can realize the stretch features and AE signals regularity, which is very important to distinguish high pressure gas cylinder detection. The AE signals are divided into four different stages through the tensile experiment, which show that AE signals and theory analysis are fitted well and thus be used to distinguish materials defects.

scholarly journals Multiple Cylinder Extraction from Organized Point Clouds

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7630
Author(s):  
Saed Moradi ◽  
Denis Laurendeau ◽  
Clement Gosselin
Keyword(s):  
Estimation Method ◽  
Closed Form Solution ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
Form Solution ◽  
Normal Vector ◽  
Open Problems ◽  
Robust Solution ◽  
Geometric Primitives ◽  
Cylinder Detection

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.

A robust and efficient framework for fast cylinder detection

2019 ◽  
Vol 117 ◽  
pp. 17-28 ◽  
Author(s):  
Rui Figueiredo ◽  
Atabak Dehban ◽  
Plinio Moreno ◽  
Alexandre Bernardino ◽  
José Santos-Victor ◽  
...  
Keyword(s):  
Cylinder Detection
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