Matrix computations (3rd ed.)
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
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
From Projective to Euclidean Space Under any Practical Situation, a Criticism of Self-Calibration
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Circular Motion Geometry Using Minimal Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Convergent Autocalibration Using Interval Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Methods and geometry for plane-based self-calibration
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Linear auto-calibration for ground plane motion
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Self-calibration from Planes Using Differential Evolution
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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We investigate the problem of self-calibrating a camera, from multiple views of a planar scene. By self-calibrating, we refer to the problem of simultaneously estimate the camera intrinsic parameters and the Euclidean structure of one 3D plane. A solution is usually obtained by solving a non-linear system via local optimization, with the critical issue of parameter initialization, especially the focal length. Arguing that these five parameters are inter-dependent, we propose an alternate problem formulation, with only three d.o.f., corresponding to three parameters to estimate. In the light of this, we are concerned with global optimization in order to get a guaranteed solution, with the shortest response time. Interval analysis provides an efficient numerical framework, that reveals to be highly performant, with regard to both estimation accuracy and time-consuming.