Robust method for camera self-calibration by an unkown planar scene

In this paper we present a self-calibration method for a CCD camera with varying intrinsic parameters based on an unknown planar scene. The advantage of our method is reducing the number of images (two images) needed to estimate the parameters of the camera used. Moreover, self-calibration equations are related to the number of points matched (very numerous and easy to detect) rather than to the number of images, since the use of a large number of images requires high computation time. On the other hand, we base on the points matched, which are numerous, when estimating the projection matrices and homographies between the images. The latter are used with the images of the absolute conic to formulate a system of non-linear equations (self-calibration equations depend on the number of matched pairs). Finally, the intrinsic parameters of the camera can be obtained by minimizing a non-linear cost function in a two-step procedure: initialization and optimization. Experiment results show the robustness of our algorithms in terms of stability and convergence.