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Guided-MLESAC: faster image transform estimation by using matching priors.

Abstract:

MLESAC is an established algorithm for maximum-likelihood estimation by random sampling consensus, devised for computing multiview entities like the fundamental matrix from correspondences between image features. A shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences. This paper explains the consequences of that omission and describes how the algorithm's theoretical standing and practical performance can be en...

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Publication status:
Published

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Publisher copy:
10.1109/tpami.2005.199

Authors


Tordoff, BJ More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Journal:
IEEE transactions on pattern analysis and machine intelligence
Volume:
27
Issue:
10
Pages:
1523-1535
Publication date:
2005-10-05
DOI:
EISSN:
1939-3539
ISSN:
0162-8828
URN:
uuid:732b9724-9d51-4451-8b3d-bb5a480a4af2
Source identifiers:
63356
Local pid:
pubs:63356

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