Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Robust recovery of the epipolar geometry for an uncalibrated stereo rig
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Performance characterization of fundamental matrix estimation under image degradation
Machine Vision and Applications - Special issue on performance evaluation
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Globally Optimal Estimates for Geometric Reconstruction Problems
International Journal of Computer Vision
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
Epipolar geometry of catadioptric stereo systems with planar mirrors
Image and Vision Computing
Visual servoing path planning via homogeneous forms and LMI optimizations
IEEE Transactions on Robotics
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
EVP-based multiple-view triangulation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Estimating the fundamental matrix using second-order cone programming
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Good match exploration using triangle constraint
Pattern Recognition Letters
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In this paper, a new method for the estimation of the fundamental matrix from point correspondences is presented. The minimization of the algebraic error is performed while taking explicitly into account the rank-two constraint on the fundamental matrix. It is shown how this nonconvex optimization problem can be solved avoiding local minima by using recently developed convexification techniques. The obtained estimate of the fundamental matrix turns out to be more accurate than the one provided by the linear criterion, where the rank constraint of the matrix is imposed after its computation by setting the smallest singular value to zero. This suggests that the proposed estimate can be used to initialize nonlinear criteria, such as the distance to epipolar lines and the gradient criterion, in order to obtain a more accurate estimate of the fundamental matrix.