NDLI: Nonlinear estimation of the fundamental matrix with minimal parameters

Content Provider	IEEE Xplore Digital Library
Author	Bartoli, A. Sturm, P.
Copyright Year	1979
Abstract	The purpose of this paper is to give a very simple method for nonlinearly estimating the fundamental matrix using the minimum number of seven parameters. Instead of minimally parameterizing it, we rather update what we call its orthonormal representation, which is based on its singular value decomposition. We show how this method can be used for efficient bundle adjustment of point features seen in two views. Experiments on simulated and real data show that this implementation performs better than others in terms of computational cost, i.e., convergence is faster, although methods based on minimal parameters are more likely to fall into local minima than methods based on redundant parameters.
Sponsorship	IEEE Computer Society
Page Count	7
File Size	933065
Starting Page	426
Ending Page	432
File Format	PDF
ISSN	01628828
Volume Number	26
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Matrix decomposition Computational efficiency Computer vision Geometry Image reconstruction Computer Society Singular value decomposition Computational modeling Convergence Cameras
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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