Camera calibration is vital in inverse Hartmann system for measuring surface shape of large aperture plane mirrors. It directly affects system accuracy. Through calibration, the internal and external parameters of camera can be acquired. In order to increase the accuracy of camera calibration, this paper selects dot matrix pattern suitable for our system as calibration plate and applies centroid detection algorithm to extract position of feature points. The impact of number and diameter size of dots on camera calibration has been analyzed. Through experiment, the repeatability of the centroid detection algorithm for dot matrix reaches 0.004 pixels, while corner extraction algorithm for checkboard is only 0.007 pixels. It indicates that using dot matrix pattern to calibrate camera is better than using a checkboard in inverse Hartmann system.
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