This paper presents a self-calibration algorithm that seeks the camera intrinsic parameters to minimize the sum of squared distances between the measured and reprojected image points. By exploiting the constraints provided by the fundamental matrices, the function to be minimized can be directly reduced to a function of the camera intrinsic parameters; thus variant camera constraints such as fixed or varying focal lengths can be easily imposed by controlling the parameters of the resulting function. We employed the simplex method to minimize the resulting function and tested the proposed algorithm on some simulated and real data. The experimental results demonstrate that our algorithm performs well for variant camera constraints and for two-view and multiple-view cases.
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