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On projection matrix identification for camera calibration

Abstract: The projection matrix identification problem is considered with application to calibration of intrinsic camera parameters. Physical and orthogonal intrinsic camera models in context of 2D and 3D data are discussed. A novel nonlinear goal function is proposed for homographic calibration method having the fast convergence of Levenberg-Marquardt optimization procedure. Three models (linear, quadratic, and rational) and four optimization procedures for their identification were compared wrt their time complexity, the projection accuracy, and the intrinsic parameters accuracy. The analysis has been performed for both, the raw and the calibrated pixel data, too. The recommended technique with the best performance in all used quality measures is the Housholder QR decomposition for the linear least square method of the linear form of projection equations.