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13

PSIVT
2009
Springer

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Compact Fundamental Matrix Computation

13 years 11 months ago

Compact Fundamental Matrix Computation

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Abstract. A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the strict maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably the most practical of all, being small and simple. By numerical experiments, we conﬁrm that our algorithm behaves as expected.

Kenichi Kanatani, Yasuyuki Sugaya

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Added	27 May 2010
Updated	27 May 2010
Type	Conference
Year	2009
Where	PSIVT
Authors	Kenichi Kanatani, Yasuyuki Sugaya

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