MINPRAN: a new robust estimator for computer vision
Stewart, C.V.
Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Oct 1995
Volume: 17,
Issue: 10
On page(s): 925-938
ISSN: 0162-8828
References Cited: 34
CODEN: ITPIDJ
INSPEC Accession Number: 5076478
Digital Object Identifier: 10.1109/34.464558
Current Version Published: 2002-08-06
Abstract
MINPRAN is a new robust estimator capable of finding good fits in
data sets containing more than 50% outliers. Unlike other techniques
that handle large outlier percentages, MINPRAN does not rely on a known
error bound for the good data. Instead, it assumes the bad data are
randomly distributed within the dynamic range of the sensor. Based on
this, MINPRAN uses random sampling to search for the fit and the inliers
to the fit that are least likely to have occurred randomly. It runs in
time O(N2+SN log N), where S is the number of random samples
and N is the number of data points. We demonstrate analytically that
MINPRAN distinguished good fits to random data and MINPRAN finds
accurate fits and nearly the correct number of inliers, regardless of
the percentage of true inliers. We confirm MINPRAN's properties
experimentally on synthetic data and show it compares favorably to least
median of squares. Finally, we apply MINPRAN to fitting planar surface
patches and eliminating outliers in range data taken from complicated
scenes
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