An effective search approach to camera parameter estimation using an arbitrary planar calibration object

In this paper we shall present a new method for camera parameter estimation using an arbitrary planar calibration object. We begin with an approximate model for the perspective view and give a parameter-free normalized-line-segment-ratio relationship between the corresponding reference and sensed views. This representation makes parameter decoupling possible and reduces the library dimension from six to one. Then the estimation problem can be viewed as a library search problem. Various mechanisms are provided to reduce the library search time, including (a) library partition by clustering, (b) sensed cluster identification by binary search, and (c) sequential testing for view matching using sorted features. In addition, feature perturbation is also considered to achieve better robustness against feature noise. Both computer-generated and real data are included in experiments, illustrating how our method works.