Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Artificial Intelligence - Special volume on 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
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
University of Glasgow at ImageCLEF 2009 robot vision task: a rule based approach
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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A new linear approach to estimating the fundamental matrix is proposed in this paper. The approach is based on the orthogonal least-squares technique for estimating the fundamental matrix. Using eigenvectors corresponding to the two smallest eigenvalues achieved by the technique mentioned above, we construct a 3x3 generalized eigenvalue problem. The solutions to the problem give not only the fundamental matrix but also the corresponding epipoles. The performance of the new approach is compared with several existing linear methods. It is shown that the approach achieves the higher accuracy.