Perspective correction using camera intrinsic parameters

2016 ◽  
Author(s):  
Xin Li ◽  
Wei Liu ◽  
Wei Fan ◽  
Jun Sun ◽  
Naoi Satoshi
Keyword(s):  
Intrinsic Parameters ◽  
Camera Intrinsic Parameters

Accurate camera intrinsic parameters calibration using virtual stereo board

2005 ◽  
Author(s):  
Zhijing Yu ◽  
Jiwei Ma ◽  
Yuquan Ma ◽  
Rensheng Che ◽  
Zhihong Li ◽  
...  
Keyword(s):  
Intrinsic Parameters ◽  
Camera Intrinsic Parameters

scholarly journals A Decoupled Calibration Method for Camera Intrinsic Parameters and Distortion Coefficients

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Kun Yan ◽  
Hong Tian ◽  
Enhai Liu ◽  
Rujin Zhao ◽  
Yuzhen Hong ◽  
...  
Keyword(s):  
Real Data ◽  
Calibration Method ◽  
Intrinsic Parameters ◽  
Radial Distortion ◽  
Vision Measurement ◽  
Field Of Vision ◽  
Marquardt Algorithm ◽  
Calibration Accuracy ◽  
Camera Intrinsic Parameters ◽  
Division Model

Camera calibration is a necessary process in the field of vision measurement. In this paper, we propose a flexible and high-accuracy method to calibrate a camera. Firstly, we compute the center of radial distortion, which is important to obtain optimal results. Then, based on the radial distortion of the division model, the camera intrinsic parameters and distortion coefficients are solved in a linear way independently. Finally, the intrinsic parameters of the camera are optimized via the Levenberg-Marquardt algorithm. In the proposed method, the distortion coefficients and intrinsic parameters are successfully decoupled; calibration accuracy is further improved through the subsequent optimization process. Moreover, whether it is for relatively small image distortion or distortion larger image, utilizing our method can get a good result. Both simulation and real data experiment demonstrate the robustness and accuracy of the proposed method. Experimental results show that the proposed method can be obtaining a higher accuracy than the classical methods.

Optimization Method for Camera Intrinsic Parameters Based on Improved Particle Swarm Algorithm

2020 ◽  
Vol 57 (4) ◽  
pp. 041514
Author(s):  
徐呈艺 Xu Chengyi ◽  
刘英 Liu Ying ◽  
肖轶 Xiao Yi ◽  
曹健 Cao Jian
Keyword(s):  
Particle Swarm ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Intrinsic Parameters ◽  
Swarm Algorithm ◽  
Camera Intrinsic Parameters

Rapid Calibration Method for 3D Laser Scanner

2020 ◽  
Vol 14 (2) ◽  
pp. 234-241
Author(s):  
Bin Liu ◽  
Qian Qiao ◽  
Fangfang Han
Keyword(s):  
Laser Scanner ◽  
Vertical Direction ◽  
Calibration Method ◽  
Intrinsic Parameters ◽  
Average Value ◽  
Light Plane ◽  
Imaging Model ◽  
Reference Target ◽  
Camera Intrinsic Parameters ◽  
Rapid Calibration

Background: The 3D laser scanner is a non-contact active-sensing system, which has a number of applications. Many patents have been filed on the technologies for calibrating 3D laser scanner. A precise calibration method is important for measuring the accuracy of the 3D laser scanner. The system model contains three categories of parameters to be calibrated which include the camera intrinsic parameters, distortion coefficients and the light plane parameters. Typically, the calibration process is completed in two steps. Based on Zhang’s method, the calibration of the camera intrinsic parameters and distortion coefficients can be performed. Then, 3D feature points on the light plane should precisely be formed and extracted. Finally, the points are used to calculate the light plane parameters. Methods: In this paper, a rapid calibration method is presented. Without any high precision auxiliary device, only one coplanar reference target is used. By using a group of captured images of the coplanar reference target placed in the field of view arbitrarily, calibration can be performed in one step. Based on the constraint from the planes formed by the target in different directions and the camera imaging model, a large amount of 3D points on the light plane can easily be obtained. The light plane equation in the camera coordinates system can be gathered by executing plane fitting to the 3D points. Results: During the experimental process, the developed 3D laser scanner was calibrated by the proposed method. Then, the measuring accuracy of the system was verified with known distance in vertical direction of 1mm with sequential shifting motion generated by precision translation stage. The average value of the measured distances was found to be 1.010mm. The standard deviation was 0.008mm. Conclusion: Experimental results prove that the proposed calibration method is simple and reliable.

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