# A New Robust Operator for Computer Vision: Application to Range
and Intensity Images

- Charles V. Stewart
- Department of Computer Science
- Rensselaer Polytechnic Institute
- Troy, New York 12180--3590
*stewart@cs.rpi.edu*

## Abstract

MINPRAN accurately reconstructs range data taken from scenes that
produce: (1) large numbers of outliers, (2) points from multiple
surfaces interspersed over large image regions, and (3) extended
regions containing only bad data. MINPRAN uses random sampling to
search for fits that minimize a criterion function which models the
probability that a fit could be due to random bad points. In a
previous paper
[TR93-21] , we showed that MINPRAN finds accurate fits, even when
more than half of the data are bad; and it avoids hallucinating fits
when all of the data are bad. However, we also showed that MINPRAN
often favors a single fit bridging multiple surfaces when the data in
an image region arise from one or more surfaces. In this paper, we
extend MINPRAN in two ways to handle the problem of multiple fits in a
region: (1) it now searches for and compares two disjoint fits to the
single best fit in a region, and (2) it reconstructs the best values
of each data point, re-applies the criterion function to each fit
using only the inliers that remain consistent with that fit, and
eliminates fits that appear random. The new version of the algorithm,
called MINPRAN2, produces extremely good results on difficult range
images and intensity images that have been corrupted with a large
number of random bad values.