| 摘要 |
A digital camera is calibrated by establishing the coordinates of at least four feature points of a pattern mounted on a planar surface. At least two, and preferably three or more, images of the planar pattern are captured at different, non-parallel orientations using the digital camera. The image coordinates of the pattern's feature points are then identified in the captured images. A closed form solution can be employed to derive all the intrinsic and extrinsic parameters needed to provide the camera calibration. Essentially, the known pattern coordinates and corresponding image coordinates are used to compute a homography for each image. Then, a process is employed that estimates the intrinsic camera parameters by analyzing the homographies associated with each image. Finally, the extrinsic parameters for each image are computed from the intrinsic parameters and the homographies. However, the images can be effected by various noise sources which could affect the accuracy of the closed form solution process. If higher accuracy is called for, a maximum likelihood inference process can be employed to either provide a more accurate first estimate, or to refine the estimates derived from the closed form solution. If radial distortion caused by the lens of the camera is also a concern, the camera parameters can be further refined by taking into account this distortion.
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