Affine Invariant-Based Classification of Inliers and Outliers for Image Matching
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
An Evaluation of Affine Invariant-Based Classification for Image Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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The core of the traditional RANSAC algorithm and its more recent efficient counterparts is the hypothesis evaluation stage, with the focus on finding the best, outlier free hypothesis. Motivated by a non-parametric ensemble techniques, we demonstrate that it proves advantageous to use the entire set of hypotheses generated in the sampling stage. We show that by studying the residual distribution of each data point with respect to the entire set of hypotheses, the problem of inlier/ outlier identification can be formulated as a classification problem. We present extensive simulations of the approach, which in the presence of a large percentage ( 50%) of outliers, provides a repeatable and, an order of magnitude more efficient method compared to the currently existing techniques. Results on widebaseline matching and fundamental matrix estimation are presented.