The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2002 vol.24)
pp: 397-401
ABSTRACT
<p>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.</p>
INDEX TERMS
stereo vision, fundamental matrix, convex optimization, linear matrix inequality
CITATION
A. Garulli, A. Vicino, G. Chesi, "Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.24, no. 3, pp. 397-401, March 2002, doi:10.1109/34.990139
164 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool