  Camera calibration is a crucial prerequisite in many applications of computer
vision. In this paper, a new, geometry-based camera calibration technique is
proposed, which resolves two main issues associated with the widely used
Zhang's method: (i) the lack of guidelines to avoid outliers in the computation
and (ii) the assumption of fixed camera focal length. The proposed approach is
based on the closed-form solution of principal lines (PLs), with their
intersection being the principal point while each PL can concisely represent
relative orientation/position (up to one degree of freedom for both) between a
special pair of coordinate systems of image plane and calibration pattern. With
such analytically tractable image features, computations associated with the
calibration are greatly simplified, while the guidelines in (i) can be
established intuitively. Experimental results for synthetic and real data show
that the proposed approach does compare favorably with Zhang's method, in terms
of correctness, robustness, and flexibility, and addresses issues (i) and (ii)
satisfactorily.
