An Efficient Preconditioner and a Modified RANSAC for Fast and Robust Feature Matching.

Standard RANSAC does not perform very well for contaminated sets, when there is a majority of outliers. We present a method that overcomes this problem by transforming the problem into a 2D position vector space, where an ordinary cluster algorithm can be used to find a set of putative inliers. This set can then easily be handled by a modified version of RANSAC that draws samples from this set only and scores using the entire set. This approach works well for moderate differences in scale and rotation. For contaminated sets the increase in performance is in several orders of magnitude. We present results from testing the algorithm using the Direct Linear Transformation on aerial images and photographs used for panographs.

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