In this paper we have presented a novel algorithm for camera calibration which is a significant improvement in mathematical simplicity, accuracy and computational efficiency in the solution of all extrinsic (external camera geometric) and intrinsic (internal camera geometric and camera optics) parameters. The method involves a direct transformation from the three dimensional (3D) object world to the two-dimensional (2D) image or sensor plane in terms of "homogeneous vector forms" for the solution of 12 extrinsic and a number of intrinsic parameters for both coplanar and non-coplanar distributions of object points. Furthermore, we have demonstrated a strong robust property of the proposed algorithm by proving (with experimental corroboration) that if the camera is calibrated with image data not compensated for image center displacement and scale factor, the proposed algorithm yields parameters that cause no errors in the computation of both image and world coordinates. In addition, we have dislcussed a new method of parameter computation under a complete lens distortion effect (including both radial and tangential distortions) by the method of constrained least squares. Analytical proofs of convergence are also given. Moreover, we have provided a new complete algorithm for the solution of all calibration parameters. Finally, we have proposed a new Incremental Model for the correspondence of tolerances between the 3D object world and the 2D image plane with and without intrinsic parameter effects. Experimental results on a coplanar set of object points have been provided to support our models.
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