Camera Calibration: A Quick and Easy Way to Determine the Scale Factor
IEEE Transactions on Pattern Analysis and Machine Intelligence
A theory of self-calibration of a moving camera
International Journal of Computer Vision
Camera calibration from spheres images
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Camera calibration using spheres: A semi-definite programming approach
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Catadioptric Camera Calibration Using Geometric Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Hi-index | 0.00 |
An occluding contour of a sphere is projected to a conic in the perspective image, and such a conic is called a sphere image. Recently, it has been discovered that each sphere image is tangent to the image of the absolute conic at two double-contact image points. The double-contact theorem describes the properties of three conics which all have double contact with another conic. This theorem provides a linear algorithm to find the another conic if these three conics are given. In this paper, the double-contact theorem is employed to interpret the properties among three sphere images and the image of the absolute conic. The image of the absolute conic can be determined from three sphere images using the double-contact theorem. Therefore, a linear calibration method using three sphere images is obtained. Only three sphere images are required, and all five intrinsic parameters are recovered linearly without making assumptions, such as, zero-skew or unitary aspect ratio. Extensive experiments on simulated and real data were performed and shown that our calibration method is an order of magnitude faster than previous optimized methods and a little faster than former linear methods while maintaining comparable accuracy.