Self-Calibration from Multiple Views with a Rotating Camera

A new practical method is given for the self-calibration of a camera. In this method, at least three images are taken from the same point in space with different orientations of the camera and calibration is computed from an analysis of point matches between the images. The method requires no knowledge of the orientations of the camera. Calibration is based on the image correspondences only. This method differs fundamentally from previous results by Maybank and Faugeras on selfcalibration using the epipolar structure of image pairs. In the method of this paper, there is no epipolar structure since all images are taken from the same point in space. Since the images are all taken from the same point in space, determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur. The calibration method is evaluated on several sets of synthetic and real image data.

[1]  Begnaud Francis Hildebrand,et al.  Introduction to numerical analysis: 2nd edition , 1987 .

[2]  Anup Basu,et al.  Active calibration: alternative strategy and analysis , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  L. Dron.  Dynamic camera self-calibration from controlled motion sequences , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Marsha Jo Hannah,et al.  Bootstrap Stereo , 1980, AAAI.

[5]  Quang-Tuan Luong.  Matrice Fondamentale et Calibration Visuelle sur l''''Environnement - Vers une plus grande autonomie , 1992 .

[6]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[7]  Michael Brady,et al.  Self-calibration of the intrinsic parameters of cameras for active vision systems , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[9]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[10]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .