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

Content Provider	IEEE Xplore Digital Library
Author	Tordoff, B.J. Murray, D.W.
Copyright Year	1979
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 enhanced by deriving estimates of these prior probabilities. Using the priors in guided-MLESAC is found to give an order of magnitude speed increase for problems where the correspondences are described by one image transformation and clutter. This paper describes two further modifications to guided-MLESAC. The first shows how all putative matches, rather than just the best, from a particular feature can be taken forward into the sampling stage, albeit at the expense of additional computation. The second suggests how to propagate the output from one frame forward to successive frames. The additional information makes guided-MLESAC computationally realistic at video-rates for correspondence sets modeled by two transformations and clutter.
Sponsorship	IEEE Computer Society
Page Count	13
File Size	1733097
Starting Page	1523
Ending Page	1535
File Format	PDF
ISSN	01628828
Volume Number	27
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image sampling Sampling methods Maximum likelihood estimation Application software Computer vision Impedance matching Optimal matching Image motion analysis Information analysis Motion analysis maximum-likelihood estimation. Index Terms- Random sampling correspondence image transformation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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