Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
International Journal of Computer Vision - Special issue on a special section on visual surveillance
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Autocalibration from Planar Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Self-Calibration of Zooming Cameras Observing an Unknown Planar Structure
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing camera focal length by zooming a single point
Pattern Recognition
Camera calibration and light source orientation from solar shadows
Computer Vision and Image Understanding
Methods and geometry for plane-based self-calibration
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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The plane-based calibration consists in recovering the internal parameters of the camera from the views of a planar pattern with a known geometric structure. The existing direct algorithms use a problem formulation based on the properties of basis vectors. They minimize algebraic distances and may require a 'good' choice of system normalization. Our contribution is to put this problem into a more intuitive geometric framework. A solution can be obtained by intersecting circles, called Centre Circles, whose parameters are computed from the world-to-image homographies. The Centre Circle is the camera centre locus when planar figures are in perpective correspondence, in accordance with a Poncelet's theorem. An interesting aspect of our formulation, using the Centre Circle constraint, is that we can easily transform the cost function into a sum of squared Euclidean distances. The simulations on synthetic data and an application with real images confirm the strong points of our method.