Standard RANSAC does not perform very well for contaminated sets, when there is a majority of outliers. We present a methodthat overcomes this problem by transforming the problem into a 2D position vector space, where an ordinary cluster algorithmcan be used to find a set of putative inliers. This set can then easily be handled by a modified version of RANSAC that drawssamples from this set only and scores using the entire set. This approach works well for moderate differences in scale androtation. For contaminated sets the increase in performance is in several orders of magnitude. We present results from testingthe algorithm using the Direct Linear Transformation on aerial images and photographs used for panographs
An Efficient Preconditioner and a Modified RANSAC for Fast and Robust Feature Matching
Anders Hast;Andrea Marchetti
2012
Abstract
Standard RANSAC does not perform very well for contaminated sets, when there is a majority of outliers. We present a methodthat overcomes this problem by transforming the problem into a 2D position vector space, where an ordinary cluster algorithmcan be used to find a set of putative inliers. This set can then easily be handled by a modified version of RANSAC that drawssamples from this set only and scores using the entire set. This approach works well for moderate differences in scale androtation. For contaminated sets the increase in performance is in several orders of magnitude. We present results from testingthe algorithm using the Direct Linear Transformation on aerial images and photographs used for panographsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
