We present a practical self-calibration algorithm that only
requires a linear projective reconstruction. Recently, many
self-calibration algorithms that use only the information in
the image have been proposed. But most algorithms require
bundle adjustments in the projective reconstruction or in
the nonlinear minimization. We overcome the sensitivity of
the self-calibration algorithms due to the image noises by
adding another constraint on the position of the principal
point. We also propose a linear initialization method based
on the property of the absolute quadric. Experimental
results using real and synthetic images demonstrate the
feasibility of the proposed algorithm.