A 1-D RHT Method Based on Symmetry Point Searching Used in Ellipse Detection

2009 ◽  
Author(s):  
Haibin Yu ◽  
Yuanyuan Liu ◽  
Keming Chen
Keyword(s):  
Symmetry Point ◽  
Ellipse Detection

Fullerenes C20-C60: Combinatorial types and symmetry point groups

2002 ◽  
Vol 47 (5) ◽  
pp. 720-722 ◽  
Author(s):  
Yu. L. Voytekhovsky ◽  
D. G. Stepenshchikov
Keyword(s):  
Symmetry Point ◽  
Point Groups

scholarly journals GsymPoint: An R Package to Estimate the Generalized Symmetry Point, an Optimal Cut-off Point for Binary Classification in Continuous Diagnostic Tests

The R Journal ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 262 ◽  
Author(s):  
Mónica López-Ratón ◽  
Elisa,M. Molanes-López ◽  
Emilio Letón ◽  
Carmen Cadarso-Suárez
Keyword(s):  
Diagnostic Tests ◽  
Binary Classification ◽  
R Package ◽  
Symmetry Point ◽  
Generalized Symmetry

scholarly journals Real-time Measurement System of Cycloduction Movement Based on Fast Ellipse Detection

2007 ◽  
Vol 127 (4) ◽  
pp. 591-598 ◽  
Author(s):  
Yuusuke Sakashita ◽  
Hironobu Fujiyoshi ◽  
Yutaka Hirata ◽  
Hisanori Takamaru ◽  
Naoki Fukaya
Keyword(s):  
Real Time ◽  
Measurement System ◽  
Time Measurement ◽  
Ellipse Detection ◽  
Real Time Measurement

A method of locating the 3D centers of retroreflectors based on deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
BinBin Zhang ◽  
Fumin Zhang ◽  
Xinghua Qu
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Measurement Techniques ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Large Field ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Ellipse Detection

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

Ellipse Detection

2005 ◽  
pp. 339-359
Author(s):  
E.R. DAVIES
Keyword(s):  
Ellipse Detection
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