GEOMETRIC INVARIANTS BASED CAMERA CALIBRATION

A new approach to camera calibration based on geometric invariants is proposed. Geometric invariants are shape descriptors that remain unchanged under geometric transformations such as projection or viewpoint change. They are widely used in computer vision because they can charaterize a object which is independent of external factors. Camera calibration is the problem of determining the relationship between the 2D image a camera perceives and the 3D information of the imaged object, including the extrinsic parameters and the intrinsic parameters. The intrinsic parameters characterize the inherent properties of the camera optics, including the focal length, the image center, the image scaling factor and the lens distortion coefficients; the extrinsic parameters of a camera indicate the position and the orientation of the camera with respect to a world coordinate system. In this paper, we propose a new method for computing the extrinsic parameters basing on the results of geometric invariants. Experimental results show that this method is simpler and more robust to noise.