Name
Abstract | ||
|---|---|---|
A robust and rapid camera calibration method is essential in vision measurement. This paper proposes a camera calibration method using a 2.5-D coding target that requires just a single captured image and is capable of identifying the intrinsic and extrinsic parameters of a camera system simultaneously. The calibration target consists of four fixed planes to which coded patterns are attached. The fixed planes of the target prevent the ill condition that arises in Zhangu0027s method, and they provide four different homography matrices at the same time. The extrinsic parameters of the cameras are calculated using the geometric constraints of the feature circles on each plane. The coded pattern allows the four planes to be distinguished automatically. The proposed method is first verified by comparison experiments with three classical calibration methods. The results confirm that the proposed method has the greatest accuracy and smallest variance of errors. The influences on the calibration results of geometric parameters such as the dihedral angle and the area ratio of the feature circles are then investigated by systematic experiments. This proposed camera calibration method using a single captured image represents a breakthrough in a camera calibration technology and has a broad potential for application to machine vision and vision measurement. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tim.2018.2884583 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Calibration,Cameras,Robustness,Feature extraction,Mathematical model,Nonlinear distortion | Computer vision,Machine vision,Matrix (mathematics),Electronic engineering,Feature extraction,Robustness (computer science),Homography,Camera resectioning,Artificial intelligence,Nonlinear distortion,Mathematics,Calibration | Journal |
Volume | Issue | ISSN |
68 | 10 | 0018-9456 |
Citations | PageRank | References |
1 | 0.41 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Zhang | 1 | 4 | 2.17 |
| Huan Yu | 2 | 46 | 13.63 |
| Huaxia Deng | 3 | 4 | 2.17 |
| Zhiwen Chai | 4 | 1 | 0.41 |
| Mengchao Ma | 5 | 5 | 2.60 |
| Xiang Zhong | 6 | 3 | 1.48 |