A Method of Camera Calibration Based on Kriging Interpolation
The basis of visual measurement is camera calibration. In the traditional calibration method, the radial and tangential distortion models usually are adopted. Because of the randomness of lens distortion, the above fixed form distortion model cannot accurately express the distortion distribution. To...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/04b5ac8900c04ddda32fff5d44263299 |
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| Sumario: | The basis of visual measurement is camera calibration. In the traditional calibration method, the radial and tangential distortion models usually are adopted. Because of the randomness of lens distortion, the above fixed form distortion model cannot accurately express the distortion distribution. To solve the above problems, a calibration method with a new distortion model is presented in the paper. First, an exact linear model is obtained, using only the corner coordinates of the image center region; then, using this model, the projection deviation of all corner points in the pixel plane can be obtained, that is, the point cloud of projection deviation distribution; finally, the Kriging interpolation method is used to obtain a continuous projection deviation distribution function which can accurately express lens distortion in the pixel plane. Using this function and the corresponding linear model, all two-dimensional image points can be accurately projected into three-dimensional space. To compare with the traditional method, the mean error of projection and measurement error are calculated in the experiment, and the experimental results show that the calibration method is more accurate and more suitable for measuring requirements. |
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