Name
Abstract | ||
|---|---|---|
In this paper, a new camera calibration method based on the image of the absolute quadratic curve (IAC) is proposed, and a new target is designed for this method, which is both convenient and flexible. It first extracts the characteristic points and the characteristic lines of the target and finds out the vanishing point and the vanishing line. The radial and tangential distortion coefficients are obtained by using the cross ratio invariance to correct the target image distortion. Then, the four internal parameters of the camera are obtained by IAC. The influence of the skew parameters is ignored. The rotation matrix is then calculated by the orthogonal characteristic of the coordinate system, and the translation vector is calculated by the center coordinates of the camera. In this way, the internal and external parameters of the camera can be obtained. The internal and external parameters are taken as initial values, and the optimal results are obtained by nonlinear optimization using the reprojection method. Finally, the relative position between different target images can be obtained by using the fundamental matrix, namely, the rotation angle. In the process of solving, the normalization method is used to improve the accuracy of data processing. Not requiring any prior information of the camera, the method has a wide range of applications. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2893660 | IEEE ACCESS |
Keywords | Field | DocType |
Camera calibration,image of the absolute quadratic curve (IAC),fundamental matrix,cross ratio invariance,vanishing point,vanishing line,distortion correction | Coordinate system,Rotation matrix,Computer science,Algorithm,Quadratic function,Camera resectioning,Translation (geometry),Distortion,Fundamental matrix (computer vision),Vanishing point,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenlei Liu | 1 | 0 | 2.03 |
| Sentang Wu | 2 | 0 | 2.70 |
| Xiaolong Wu | 3 | 6 | 3.14 |
| hongbo zhao | 4 | 1 | 4.75 |