Linear Camera Self-Calibration with Varying Intrinsic Parameters Based on Projective Reconstruction

Recently, self-calibration algorithms that use only the information in the image have been actively researched. But most algorithms require bundle adjustment in the projective reconstruction or in the nonlinear minimization. In some vision systems, the camera's position and optic system require constant adjustment, and the camera must re-calibration after every adjustment. Aimed at this circumstance, we proposes a practical camera self-calibration algorithm that only require a linear projective reconstruction to deal with the situation when the camera's distorted skews and principal points are already known, while the other intrinsic parameters varies. That is, to calculate the fundamental matrix between images to obtain projective reconstruction at first, and on which basis then, to regain the homography matrix by linear method, lastly, to obtain the camera's intrinsic parameters using homography matrix. Simulation results have demonstrated the feasibility of the proposal algorithm.