IPSJ Transactions on Computer Vision and Applications
Online ISSN : 1882-6695
ISSN-L : 1882-6695
Compact Fundamental Matrix Computation
Kenichi KanataniYasuyuki Sugaya
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2010 Volume 2 Pages 59-70

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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 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.
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© 2010 by the Information Processing Society of Japan
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