Mathematical elements for computer graphics (2nd ed.)
Mathematical elements for computer graphics (2nd ed.)
Some Properties of the E Matrix in Two-View Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A theory of self-calibration of a moving camera
International Journal of Computer Vision
Canonical representations for the geometries of multiple projective views
Computer Vision and Image Understanding
Kruppa's Equations Derived from the Fundamental Matrix
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
Sequential Updating of Projective and Affine Structure from Motion
International Journal of Computer Vision
A Case Against Kruppa's Equations for Camera Self-Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An Algorithm for Global Optimization using the Taylor–Bernstein Form as Inclusion Function
Journal of Global Optimization
Estimation of Relative Camera Positions for Uncalibrated Cameras
ECCV '92 Proceedings of the Second European Conference on Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
On Computing Metric Upgrades of Projective Reconstructions Under the Rectangular Pixel Assumption
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Minimal Conditions on Intrinsic Parameters for Euclidean Reconstruction
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Autocalibration and the absolute quadric
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Stratified Approach to Metric Self-Calibration
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Euclidean Reconstruction from Constant Intrinsic Parameters
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Fast and Accurate Self-Calibration
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Camera calibration based on receptive fields
Pattern Recognition
Globally Optimal Estimates for Geometric Reconstruction Problems
International Journal of Computer Vision
A matter of notation: Several uses of the Kronecker product in 3D computer vision
Pattern Recognition Letters
Computer Vision and Image Understanding
Camera self-calibration from bivariate polynomial equations and the coplanarity constraint
Image and Vision Computing
Globally Optimal Algorithms for Stratified Autocalibration
International Journal of Computer Vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Maximum likelihood autocalibration
Image and Vision Computing
A simple solution to the six-point two-view focal-length problem
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Towards a guaranteed solution to plane-based self-calibration
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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We address the problem of autocalibration of a moving camera with unknown constant intrinsic parameters. Existing autocalibration techniques use numerical optimization algorithms whose convergence to the correct result cannot be guaranteed, in general. To address this problem, we have developed a method where an interval branch-and-bound method is employed for numerical minimization. Thanks to the properties of Interval Analysis this method converges to the global solution with mathematical certainty and arbitrary accuracy and the only input information it requires from the user are a set of point correspondences and a search interval. The cost function is based on the Huang-Faugeras constraint of the essential matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, experimental results are presented.