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| G. Chesi, A. Garulli, A. Vicino, R. Cipolla, "Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 397-401, March, 2002. | |||
| BibTex | x | ||
| @article{ 10.1109/34.990139, author = {G. Chesi and A. Garulli and A. Vicino and R. Cipolla}, title = {Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {24}, number = {3}, issn = {0162-8828}, year = {2002}, pages = {397-401}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.990139}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach IS - 3 SN - 0162-8828 SP397 EP401 EPD - 397-401 A1 - G. Chesi, A1 - A. Garulli, A1 - A. Vicino, A1 - R. Cipolla, PY - 2002 KW - stereo vision KW - fundamental matrix KW - convex optimization KW - linear matrix inequality VL - 24 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
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.
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