Study on Axis Calibration Method of the Coordinate Measurement System Based on Binocular Vision

2014 ◽  
Vol 989-994 ◽  
pp. 3266-3269
Author(s):  
Zi Miao Zhang ◽  
Shi Hai Zhang ◽  
Ya Nan Yu
Keyword(s):  
Binocular Vision ◽  
Measurement System ◽  
Rotation Axis ◽  
Vision System ◽  
Calibration Method ◽  
Reference Points ◽  
Calibration Model ◽  
Coordinate Measurement ◽  
Rotation Stage ◽  
Binocular Vision System

For a coordinate measurement system based on binocular vision, system calibration is an important factor for measurement accuracy. In this paper we present a flexible calibration method for the axis calibration of rotation stage which is installed in front of the binocular vision system to increase the system measurement range. By putting a standard ball in front of the binocular vision system, a sequence of pictures is taken by the two cameras with a few different rotation angles of the rotation stage. With the method of space intersection of two straight lines, the reference points (the ball centers at each rotation angles) for axis calibration are figured out. The trail of standard ball is a circle. Since all ball centers of rotation are on a plane perpendicular to the axis, the center of circle is on the axis of rotation stage. The rotation axis of stage is then calibrated according to the calibration model.

Optimization of structural parameters of binocular vision system in remote 3-D coordinate measurement

2015 ◽  
Vol 23 (10) ◽  
pp. 2902-2908 ◽  
Author(s):  
王向军 WANG Xiang-jun ◽  
卞越新 BIAN Yue-xin ◽  
刘峰 LIU Feng ◽  
吴凡路 WU Fan-lu
Keyword(s):  
Binocular Vision ◽  
Vision System ◽  
Structural Parameters ◽  
Coordinate Measurement ◽  
Binocular Vision System

A Calibration Method for Binocular Vision System Based on CPSO-BP Netural Network

2014 ◽  
Vol 490-491 ◽  
pp. 1686-1691
Author(s):  
Jun Zhou ◽  
Tao Xia ◽  
Ting Ting Wang ◽  
Hua Li Li ◽  
Yu Ping Fu
Keyword(s):  
Neural Network ◽  
Binocular Vision ◽  
Bp Neural Network ◽  
Vision System ◽  
Back Propagation ◽  
Calibration Method ◽  
Image Feature ◽  
Training Set ◽  
Swarm Optimization ◽  
Binocular Vision System

This paper presents a new calibration method for binocular vision system, based on CPSO-BP neural network. Firstly, the training set of the back propagation (BP) neural network is formed by the image feature point extracted from the binocular vision system. Then the cooperate particle swarm optimization (CPSO) algorithm is introduced to optimize the weights of the BP neural network, making the network with a stronger ability of the global optimization. Experimental results demonstrate that the proposed CPSO-BP-based algorithm has a higher calibration precision than the traditional BP-based calibration method.

Easy conductive calibration method for binocular vision system based on collinear image transformation

2017 ◽  
Vol 56 (10) ◽  
pp. 1 ◽  
Author(s):  
Enkun Cui ◽  
YanJie Wang ◽  
Tao Zhang ◽  
Nan Di ◽  
YanHe Yin ◽  
...  
Keyword(s):  
Binocular Vision ◽  
Vision System ◽  
Calibration Method ◽  
Image Transformation ◽  
Binocular Vision System

Single-image calibration method for multiple virtual binocular vision system

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 261-268 ◽  
Author(s):  
Xinghua Chai ◽  
Fuqiang Zhou ◽  
Yan Hu ◽  
Xin Chen
Keyword(s):  
Binocular Vision ◽  
Vision System ◽  
Calibration Method ◽  
Single Image ◽  
Binocular Vision System ◽  
Image Calibration

Calibration Method of Binocular Vision System with Zoom Lens Based on Homography Matrix

2018 ◽  
Vol 38 (3) ◽  
pp. 0315005
Author(s):  
姜涛 Jiang Tao ◽  
程筱胜 Cheng Xiaosheng ◽  
崔海华 Cui Haihua ◽  
贾华宇 Jia Huayu ◽  
张逢骏 Zhang Fengjun
Keyword(s):  
Binocular Vision ◽  
Vision System ◽  
Calibration Method ◽  
Zoom Lens ◽  
Binocular Vision System

New Camera Parameters Calibration Method of Binocular Vision System

2013 ◽  
Vol 712-715 ◽  
pp. 2372-2377
Author(s):  
Hong E Ren ◽  
Si Li
Keyword(s):  
Binocular Vision ◽  
Theoretical Basis ◽  
Simulation Experiment ◽  
Vision System ◽  
Focal Length ◽  
Calibration Method ◽  
Camera Parameters ◽  
Relationship Of ◽  
The Relationship ◽  
Binocular Vision System

About the key problems of the binocular vision system design which was used to video log examining, and according to the binocular parallax theory and triangle measuring theory, the paper researched the basic principle of camera imaging, proposed the camera parameters calibration method that could determine the best placed baseline distance of two cameras and the camera focal length together, inferred the relationship of the best baseline distance and the camera focal length by analyzing to many groups calibration results. The simulation experiment results show that this method is simple, practical, and has a high precision, provides a theoretical basis for design of tow cameras positions in the binocular vision system.

scholarly journals Research on Calibration Method of Binocular Vision System Based on Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hao Zhu ◽  
Mulan Wang ◽  
Weiye Xu
Keyword(s):  
Neural Network ◽  
Binocular Vision ◽  
Vision System ◽  
Calibration Method ◽  
Correction Method ◽  
Distortion Correction ◽  
Inspection System ◽  
Lens Distortion ◽  
Calibration Methods ◽  
Binocular Vision System

In binocular vision inspection system, the calibration of detection equipment is the basis to ensure the subsequent detection accuracy. The current calibration methods have the disadvantages of complex calculation, low precision, and poor operability. In order to solve the above problems, the calibration method of binocular camera, the correction method of lens distortion, and the calibration method of projector in the binocular vision system based on surface structured light are studied in this paper. For lens distortion correction, on the basis of analyzing the traditional correction methods, a distortion correction method based on radial basis function neural network is proposed. Using the excellent nonlinear mapping ability of RBF neural network, the distortion correction models of different lenses can be obtained quickly. It overcomes the defect that the traditional correction model cannot adjust adaptively with the type of lens. The experimental results show that the accuracy of the method can meet the requirements of system calibration.

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