Abstract
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.
Highlights
Camera calibration is the process of solving the internal and external parameters and distortion coefficient of the camera by the three-dimensional coordinates of the space target point and its two-dimensional projection coordinates[1,2,3,4,5,6,7]
Camera calibration is a key step in machine vision, and its accuracy has a direct impact on the measurement accuracy of the vision system[8]
Camera calibration methods can be divided into two types, which are the target-based calibration methods and the self-calibration methods
Summary
Camera calibration is the process of solving the internal and external parameters and distortion coefficient of the camera by the three-dimensional coordinates of the space target point and its two-dimensional projection coordinates[1,2,3,4,5,6,7]. A more simple, flexible, and high-accuracy method was proposed by Zhang, in which a non-linear method is used to solve the distortion parameters[13]. This method does not require an expensive calibration model and is highly practical. Most of the present calibration methods are based on the Zhang's camera model. A geometry-based camera calibration technique is proposed by Jen-Hui Chuang in [17], which improves the speed of solving the model parameters. The camera parameters with the distortion-free camera model is obtained by improved Zhang's method, which only uses the central region points of the image.
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