In this article we present a practical selfcalibration method of camera having variable intrinsic parameters. This method is an extension of a method which we have already published. The main idea of our method addressed in this paper is the use of interest points automatically detected in two images of an unknown 3D scene to self-calibrate the camera. These interest points are the projections of the vertices of unknown 3D parallelograms (each triplet of interest points is the projections of three vertices of a unique 3D parallelogram). The projection of the vertices of parallelograms in the two image planes of the camera allows us to obtain two equations based on the intrinsic camera parameters. Seeing that our camera can change its intrinsic parameters from a view to another so the number of unknowns is ten hence the need to use at least five parallelograms in the scene to obtain other equations in function of intrinsic camera parameters. The paper tests the performance of the proposed approach on the both synthetic and real data.
-In this article we proposed a novel approach to selfcalibrate a camera with variable focal length. We show that the estimation of camera's intrinsic parameters is possible from only two points of an unknown planar scene. The projection of these points by using the projection matrices in two images only permit us to obtain a system of equations according to the camera's intrinsic parameters . From this system we formulated a nonlinear cost function which its minimization allows us to estimate the camera's intrinsic parameters in each view. The results on synthetic and real data justify the robustness of our method in term of reliability and convergence.
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