@Article{stewart:pami95,
author = {C.\ V.\ Stewart},
title = {MINPRAN: a new robust estimator for computer vision},
journal = {Pattern Analysis and Machine Intelligence, IEEE
Transactions on},
year = 1995,
volume = 17,
number = 10,
pages = {925--938},
keywords = {computational complexity, computer vision, image
reconstruction, least mean squares methods,
parameter estimation, surface fitting, MINPRAN,
complicated scenes, data sets, error bound, inliers,
least median of squares, outliers, planar surface
patches, random sampling, robust estimator, sensor,
surface reconstruction, synthetic data},
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(N^{2}+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},
issn = {0162-8828},
annote = {}
}