Fundamental matrix (computer vision)

Known as: Bifocal tensor, Fundamental matrix, Projective reconstruction theorem 
In computer vision, the fundamental matrix is a 3×3 matrix which relates corresponding points in stereo images. In epipolar geometry, with… (More)
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Highly Cited
2005
Highly Cited
2005
This paper describes a direct, self-contained method for planar image rectification of stereo pairs. The method is based solely… (More)
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Highly Cited
2004
Highly Cited
2004
Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the… (More)
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Highly Cited
2000
Highly Cited
2000
We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data… (More)
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Highly Cited
1999
Highly Cited
1999
Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the… (More)
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Highly Cited
1997
Highly Cited
1997
This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the… (More)
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Highly Cited
1997
Highly Cited
1997
The purpose of this note is to give a specific form for Kruppa’s equations in terms of the Fundamental matrix. Kruppa’s equations… (More)
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Highly Cited
1997
Highly Cited
1997
This paper deals with the analysis of the uncertainty of the fundamental matrix. The basic idea is to compute the fundamental… (More)
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Highly Cited
1996
Highly Cited
1996
In this paper we analyze in some detail the geometry of a pair of cameras, i.e., a stereo rig. Contrarily to what has been done… (More)
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Highly Cited
1995
Highly Cited
1995
New methods are reported for the detection of multiple solutions (degeneracy) when estimating the fundamental matrix, with… (More)
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Highly Cited
1993