Abstract
A new method for estimating the fundamental matrix is proposed. Using eigenvectors corresponding to the two smallest eigenvalues obtained by the orthogonal least-squares technique, we construct a 3 × 3 generalized eigenvalue problem. Its solution gives not only the fundamental matrix but also the corresponding epipoles. The new method performs well as compared with several existing linear methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
REFERENCES
Q.-T. Luong and O.D. Faugeras (1996), The fundamental matrix: theory, algorithms, and stability analysis. International Journal of Computer Vision, 17,1, pp. 43–75.
X. Armangué and J. Salvi (2003), Overall view regarding fundamental matrix estimation. Image and Vision Computing 21, 2, pp. 205–220.
Z.Y. Zhang (1998), Determining the epipolar geometry and its uncertainty: a review. International Journal of Computer Vision, 27, 2, pp. 161–198.
R.I. Hartley and A. Zisserman (2000), Multiple View Geometry in Computer Vision, Cambridge University Press, London.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Zhong, H., Feng, Y., Pang, Y. (2006). Fundamental Matrix Estimation Based On A Generalized Eigenvalue Problem. In: LIU, G., TAN, V., HAN, X. (eds) Computational Methods. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-3953-9_144
Download citation
DOI: https://doi.org/10.1007/978-1-4020-3953-9_144
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3952-2
Online ISBN: 978-1-4020-3953-9
eBook Packages: EngineeringEngineering (R0)