A flexible technique based on fundamental matrix for camera self-calibration with variable intrinsic parameters from two views

• Self-calibrate cameras with varying focal length.
• Automatic estimation of the intrinsic parameters of the camera.
• Works freely in the domain of self-calibration without any prior knowledge about the scene or on the cameras.

We propose a new self-calibration technique for cameras with varying intrinsic parameters that can be computed using only information contained in the images themselves. The method does not need any a priori knowledge on the orientations of the camera and is based on the use of a 3D3D scene containing an unknown isosceles right triangle. The importance of our approach resides at minimizing constraints on the self-calibration system and the use of only two images to estimate these parameters. This method is based on the formulation of a nonlinear cost function from the relationship between two matches which are the projection of two points representing vertices of an isosceles right triangle, and the relationship between the images of the absolute conic. The resolution of this function enables us to estimate the cameras intrinsic parameters. The algorithm is implemented and validated on several sets of synthetic and real image data.