Self-calibration of Varying Internal Camera Parameters Algorithm Based on Quasi-affine Reconstruction

This paper presents an method of self-calibration of varying internal camera parameters that based on quasi-affine reconstruction. In a stratified approach to self-calibration, a projective reconstruction is obtained first and this is successively refined first to an affine and then to a Euclidean reconstruction. It has been observed that the difficult step is to obtain the affine reconstruction, or equivalently to locate the plane at infinity in the projective coordinate frame. So, a quasi-affine reconstruction is obtained first by image sequences, then we can obtain the infinite plane in the quasi-affine space, and equivalently to affine reconstruction. Then the infinite homography matrix can be calculated through the affine reconstruction, and then using the infinite homography matrix and constraints of the image of absolute conic to calculate the camera internal parameters matrix, and further to measure the metric rreconstruction. This method does not require a special scene constraints(such as prapllel, perpendicular) information, and also the camera movement informations(such as pure translation or orthogonal movement ), to achieve the goal of self-calibration. The theoretics analysis and experiments with real data demonstrate that this self-calibration method is available, stable and robust.