Memory-efficient circle and ellipse detection algorithm in digital images

2002 ◽  
Author(s):  
W.H.W. Yu
Keyword(s):  
Digital Images ◽  
Detection Algorithm ◽  
Ellipse Detection ◽  
Memory Efficient

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.

scholarly journals Fast and robust ellipse detection algorithm for head-mounted eye tracking systems

2018 ◽  
Vol 29 (5) ◽  
pp. 845-860 ◽  
Author(s):  
Ion Martinikorena ◽  
Rafael Cabeza ◽  
Arantxa Villanueva ◽  
Iñaki Urtasun ◽  
Andoni Larumbe
Keyword(s):  
Eye Tracking ◽  
Detection Algorithm ◽  
Tracking Systems ◽  
Ellipse Detection

An ellipse detection algorithm based on edge classification

2015 ◽  
Author(s):  
Liu Yu ◽  
Feng Chen ◽  
Jianming Huang ◽  
Xiangquan Wei
Keyword(s):  
Detection Algorithm ◽  
Ellipse Detection ◽  
Edge Classification

Ellipse Detection-Based Bin-Picking Visual Servoing System

2013 ◽  
pp. 114-121
Author(s):  
Kai Liu ◽  
Hongbo Li ◽  
Zengqi Sun
Keyword(s):  
Visual Servoing ◽  
Geometric Model ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Time Performance ◽  
Ellipse Detection ◽  
Bin Picking ◽  
Random Sample Consensus ◽  
Hole Model ◽  
Ransac Algorithm

In this chapter, the authors tackle the task of picking parts from a bin (bin-picking task), employing a 6-DOF manipulator on which a single hand-eye camera is mounted. The parts are some cylinders randomly stacked in the bin. A Quasi-Random Sample Consensus (Quasi-RANSAC) ellipse detection algorithm is developed to recognize the target objects. Then the detected targets’ position and posture are estimated utilizing camera’s pin-hole model in conjunction with target’s geometric model. After that, the target, which is the easiest one to pick for the manipulator, is selected from multi-detected results and tracked while the manipulator approaches it along a collision-free path, which is calculated in work space. At last, the detection accuracy and run-time performance of the Quasi-RANSAC algorithm is presented, and the final position of the end-effecter is measured to describe the accuracy of the proposed bin-picking visual servoing system.

A robust detection algorithm for copy-move forgery in digital images

2012 ◽  
Vol 214 (1-3) ◽  
pp. 33-43 ◽  
Author(s):  
Yanjun Cao ◽  
Tiegang Gao ◽  
Li Fan ◽  
Qunting Yang
Keyword(s):  
Digital Images ◽  
Detection Algorithm ◽  
Robust Detection
Sign in / Sign up

Export Citation Format

Share Document