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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... (Update)
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BibTeX entry: (Update)
@misc{ using-guidedmlesac,
author = "Estimation By Using",
title = "Guided-MLESAC: faster image transform",
url = "citeseer.ist.psu.edu/749996.html" }
Citations (may not include all citations):510 Robust Statistics (context) - Huber - 1985
377 Robust regression and outlier detection (context) - Rousseeuw, Leroy - 1987
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44 Canonical representations for the geometries of multiple pro.. - Luong, Vieville - 1996
39 Affine analysis of image sequences (context) - Shapiro - 1995
39 MLESAC: A new robust estimator with application to estimatin.. - Torr, Zisserman - 2000
30 Kluwer Academic Publishers (context) - Xu, Zhang et al. - 1996
26 Random sample concensus: A paradigm for model fitting with a.. (context) - Fischler, Bolles - 1981
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10 Linear self-calibration of a rotating and zooming camera - de Agapito, Hartley et al. - 1999
2 Using RANSAC for omnidirectional camera model fitting (context) - Micusik, Pajdla - 2003
1 Locally optimized RANSAC (context) - Chum, Matas et al. - 2003
1 Enhancing RANSAC by generalized model optimization (context) - Chum, Matas et al. - 2004
1 Controlling Zoom in Active Vision (context) - Tordoff - 2002
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