A highly robust estimator through partially likelihood functionmodeling and its application in computer vision
Zhuang, X.
Wang, T.
Zhang, P.
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jan 1992
Volume: 14,
Issue: 1
On page(s): 19-35
ISSN: 0162-8828
References Cited: 23
CODEN: ITPIDJ
INSPEC Accession Number: 4118903
Digital Object Identifier: 10.1109/34.107011
Current Version Published: 2002-08-06
Abstract
The authors present a highly robust estimator, known as the model
fitting (MF) estimator for general regression. They explain that high
robustness becomes possible through partially but completely modeling
the unknown log likelihood function. The partial modeling takes place by
taking the Bayesian statistical decision rule and a number of important
heuristics into consideration while maximizing the log likelihood
function. Applications include the automatic selection of multiple
thresholds, single rigid motion estimation or multiple rigid motion
segmentation, and estimation from two perspective views. It is believed
that the proposed MF estimator will aid in solving many robust
estimation problems that demand an estimator that is either highly
robust or capable of handling contaminated Gaussian mixture models
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