ANSAC: Adaptive Non-Minimal Sample and Consensus

Victor Fragoso, Christopher Sweeney, Pradeep Sen and Matthew Turk

Abstract

While RANSAC-based methods are robust to incorrect image correspondences (outliers), their hypothesis generators are not robust to correct image correspondences (inliers) with positional error (noise). This slows down their convergence because hypotheses drawn from a minimal set of noisy inliers can deviate significantly from the optimal model. This work addresses this problem by introducing ANSAC, a RANSAC-based estimator that accounts for noise by adaptively using more than the minimal number of correspondences required to generate a hypothesis. ANSAC estimates the inlier ratio (the fraction of correct correspondences) of several ranked subsets of candidate correspondences and generates hypotheses from them. Its hypothesis-generation mechanism prioritizes the use of subsets with high inlier ratio to generate high-quality hypotheses. ANSAC uses an early termination criterion that keeps track of the inlier ratio history and terminates when it has not changed significantly for a period of time.

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DOI

10.5244/C.31.43
https://dx.doi.org/10.5244/C.31.43

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Bibtex

            @inproceedings{BMVC2017_43,
                title={ANSAC: Adaptive Non-Minimal Sample and Consensus},
                author={Victor Fragoso, Christopher Sweeney, Pradeep Sen and Matthew Turk},
                year={2017},
                month={September},
                pages={43.1-43.11},
                articleno={43},
                numpages={11},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.43},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.43}
            }