A valid camera calibration based on the maximum likelihood using virtual stereo calibration pattern

In camera calibration, surprisingly little attention has been paid to the whole calibration process, i.e., feature points extraction from image, the precision of the calibration pattern and the calibration method. They are of the same importance for the calibration result. To satisfy the special request of vision measurement system for camera calibration parameters, we present a valid camera calibration based on the maximum likelihood criterion using high precision virtual stereo calibration pattern, which is formed by moving an infrared light-emitting diode (IR LED) feature point with CMM on pre-defined paths. Radial distortion and decentering distortion are modled. By using bilinear interpolation square-gray weighted centroid location algorithm we can accurately determined the imaging centers of feature points image coordinate. During the calibration process, we adopt a linear parameter estimation and nonlinear rerinement based on the maximum likelihood criterion. The initial parameters values are computed linearly and the final values are obtained with nonlinear minimization based on the maximum likelihood criterion. With the accurate image coordinate and the space coordinate, this method can rapidly and validly converge, and experiment results show the maximum projection distance is 0.091mm, the mean projection distance is 0.0259mm, this calibration results can satisfy the request of the vision measurement system.