1,721,177 research outputs found
Projective rectification without epipolar geometry
No full textWe present a novel algorithm performing projective rectification which does not require explicit computation of the epipolar geometry, and specifically of the fundamental matrix. Instead of finding the epipoles and computing two homographies mapping the epipoles to infinity, as done in recent work on projective rectification, we exploit the fact that the fundamental matrix of a pair of rectified images has a particular, known form. This allows us to set up a minimization that yields the rectifying homographies directly from image correspondences. Experimental results show that our method works quite robustly even in the presence of noise, and with inaccurate point correspondences. The code of our implementation will be made available at the author's web site.</p
Robust estimation of motion, structure and focal length from two views of a translating scene
No full textWe present a simple algorithm to recover focal-length, motion parameters and three-dimensional structure of an object translating along an unknown direction. The algorithm can be useful for inspection tasks for conveyor-belt systems. In order to obtain a fully metric reconstruction our algorithm uses two non-linear constraints easily available in a real setup. The algorithm is made robust performing a preliminary outlier detection. © 1999 Elsevier Science B.V.</p
On projective rectification
No full textWe present a novel algorithm performing projective rectification which does not require explicit computation of the epipolar geometry, and specifically of the fundamental matrix. Instead of finding the epipoles and computing two homographies mapping the epipoles to infinity, as done in recent work on projective rectification, we exploit the fact that the fundamental matrix of a pair of rectified images has a particular, known form. This allows us to set up a minimization that yields the rectifying homographies directly from image correspondences. Experimental results show that our method works quite robustly even in the presence of noise, and can cope with inaccurate point correspondences.</p
A general rank-2 parameterization of the fundamental matrix
No full textAll the methods for estimating the fundamental matrix do not naturally exploit the rank-2 constraint. For these reason some few rank-2 parameterizations of the fundamental matrix have been proposed over the years. In general they can be or an over-parameterization (12 parameters) and being generally valid, or use a minimal set of parameters (eight) but do not cover all the rank-2 matrices. We propose a new rank-2 parameterization which uses only 9 parameters, one more of the minimal parameterizations, and covers all the rank-2 matrices. © 2000 IEEE.</p
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