High accuracy fundamental matrix computation and its performance evaluation (2006)
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| Venue: | Proc. 17th British Machine Vision Conf (BMVC 2006), vol.1 |
| Citations: | 9 - 7 self |
BibTeX
@INPROCEEDINGS{Kanatani06highaccuracy,
author = {Kenichi Kanatani and Yasuyuki Sugaya},
title = {High accuracy fundamental matrix computation and its performance evaluation},
booktitle = {Proc. 17th British Machine Vision Conf (BMVC 2006), vol.1},
year = {2006},
pages = {217--226}
}
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Abstract
We compare the convergence performance of different numerical schemes for computing the fundamental matrix from point correspondences over two images. First, we state the problem and the associated KCR lower bound. Then, we describe the algorithms of three well-known methods: FNS, HEIV, and renormalization, to which we add Gauss-Newton iterations. For initial values, we test random choice, least squares, and Taubin’s method. Experiments using simulated and real images reveal different characteristics of each method. Overall, FNS exhibits the best convergence performance. 1
