A physical based underwater imaging model and camera calibration method

Author(s):  
Xiaojun Wu ◽  
Xingcan Tang
2019 ◽  
Vol 16 (5) ◽  
pp. 172988141986446
Author(s):  
Xiaojun Wu ◽  
XingCan Tang

Light changes its direction of propagation before entering a camera enclosed in a waterproof housing owing to refraction, which means that perspective imaging models in the air cannot be directly used underwater. In this article, we propose an accurate binocular stereo measurement system in an underwater environment. First, based on the physical underwater imaging model without approximation and Tsai’s calibration method, the proposed system is calibrated to acquire the extrinsic parameters, as the internal parameters can be pre-calibrated in air. Then, based on the calibrated camera parameters, an image correction method is proposed to convert the underwater images to air images. Thus, the epipolar constraint can be used to search the matching point directly. The experimental results show that the proposed method in this article can effectively eliminate the effect of refraction in the binocular vision and the measurement accuracy can be compared with the measurement result in air.


2011 ◽  
Vol 301-303 ◽  
pp. 1145-1150 ◽  
Author(s):  
Wang Yuan ◽  
Gang Yi Jiang ◽  
Yi Gang Wang ◽  
Mei Yu ◽  
Feng Shao ◽  
...  

Camera calibration is one of the key technologies of Computer Vision. This paper presented a microscope camera calibration method based on grid corner detection for the particular application area of microscopic measurement. The method considered not only the radial distortion, but also other non-linear factors, such as centrifugal distortion and thin-prism distortion. First of all, the actual corner coordinates information of the grid was obtained through the improved corner detection method. Then, the matrixes of lens distortion parameters and camera internal parameters were gotten according to the camera imaging model. Finally, through the established non-linear camera model, the average error between fore-projection and re-projection grid corner coordinates was obtained by re-projecting the grid corner coordinates. Experimental results show that the corner detection algorithm is accurate and the final calibration error is 0.6319 pixel, which is applicable to microscopic camera calibration.


2013 ◽  
Vol 475-476 ◽  
pp. 184-187
Author(s):  
Wen Guo Li ◽  
Shao Jun Duan

We present a camera calibration method based on circle plane board. The centres of circles on plane are regarded as the characteristic points, which are used to implement camera calibration. The proposed calibration is more accurate than many previous calibration algorithm because of the merit of the coordinate of circle centre being obtained from thousand of of edge pionts of ellipse, which is very reliable to image noise caused by edge extraction algorithm. Experiments shows the proposed algorithm can obtain high precise inner parameters, and lens distortion parameters.


2014 ◽  
Vol 568-570 ◽  
pp. 320-325 ◽  
Author(s):  
Feng Shan Huang ◽  
Li Chen

A new CCD camera calibration method based on the translation of Coordinate Measuring Machine (CMM) is proposed. The CMM brings the CCD camera to produce the relative translation with respect to the center of the white ceramic standard sphere along the X, Y, Z axis, and the coordinates of the different positions of the calibration characteristic point in the probe coordinate system can be generated. Meanwhile, the camera captures the image of the white ceramic standard sphere at every position, and the coordinates of the calibration characteristic point in the computer frame coordinate system can be registered. The calibration mathematic model was established, and the calibration steps were given and the calibration system was set up. The comparing calibration result shows that precision of this method is equivalent to that of the special calibration method, and the difference between the calibrating data of these two methods is within ±1μm.


2018 ◽  
Vol 10 (8) ◽  
pp. 1298 ◽  
Author(s):  
Lei Yin ◽  
Xiangjun Wang ◽  
Yubo Ni ◽  
Kai Zhou ◽  
Jilong Zhang

Multi-camera systems are widely used in the fields of airborne remote sensing and unmanned aerial vehicle imaging. The measurement precision of these systems depends on the accuracy of the extrinsic parameters. Therefore, it is important to accurately calibrate the extrinsic parameters between the onboard cameras. Unlike conventional multi-camera calibration methods with a common field of view (FOV), multi-camera calibration without overlapping FOVs has certain difficulties. In this paper, we propose a calibration method for a multi-camera system without common FOVs, which is used on aero photogrammetry. First, the extrinsic parameters of any two cameras in a multi-camera system is calibrated, and the extrinsic matrix is optimized by the re-projection error. Then, the extrinsic parameters of each camera are unified to the system reference coordinate system by using the global optimization method. A simulation experiment and a physical verification experiment are designed for the theoretical arithmetic. The experimental results show that this method is operable. The rotation error angle of the camera’s extrinsic parameters is less than 0.001rad and the translation error is less than 0.08 mm.


Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4643
Author(s):  
Sang Jun Lee ◽  
Jeawoo Lee ◽  
Wonju Lee ◽  
Cheolhun Jang

In intelligent vehicles, extrinsic camera calibration is preferable to be conducted on a regular basis to deal with unpredictable mechanical changes or variations on weight load distribution. Specifically, high-precision extrinsic parameters between the camera coordinate and the world coordinate are essential to implement high-level functions in intelligent vehicles such as distance estimation and lane departure warning. However, conventional calibration methods, which solve a Perspective-n-Point problem, require laborious work to measure the positions of 3D points in the world coordinate. To reduce this inconvenience, this paper proposes an automatic camera calibration method based on 3D reconstruction. The main contribution of this paper is a novel reconstruction method to recover 3D points on planes perpendicular to the ground. The proposed method jointly optimizes reprojection errors of image features projected from multiple planar surfaces, and finally, it significantly reduces errors in camera extrinsic parameters. Experiments were conducted in synthetic simulation and real calibration environments to demonstrate the effectiveness of the proposed method.


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