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Open AccessArticle

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

1
Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
2
Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
3
Department of Applied Geomatics, Université de Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC J1K 2R1, Canada
*
Author to whom correspondence should be addressed.
Algorithms 2017, 10(3), 87; https://doi.org/10.3390/a10030087
Received: 1 July 2017 / Revised: 25 July 2017 / Accepted: 25 July 2017 / Published: 27 July 2017
In this paper, a robust technique based on a genetic algorithm is proposed for estimating two-view epipolar-geometry of uncalibrated perspective stereo images from putative correspondences containing a high percentage of outliers. The advantages of this technique are three-fold: (i) replacing random search with evolutionary search applying new strategies of encoding and guided sampling; (ii) robust and fast estimation of the epipolar geometry via detecting a more-than-enough set of inliers without making any assumptions about the probability distribution of the residuals; (iii) determining the inlier-outlier threshold based on the uncertainty of the estimated model. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC (random sample consensus), MSAC, MLESAC, Cov-RANSAC, LO-RANSAC, StaRSAC, Multi-GS RANSAC and least median of squares (LMedS). Experimental results showed that the proposed approach performed better than other methods regarding the accuracy of inlier detection and epipolar-geometry estimation, as well as the computational efficiency for datasets majorly contaminated by outliers and noise. View Full-Text
Keywords: sparse matching; outlier detection; genetic algorithm; epipolar geometry; evolutionary search; guided sampling; adaptive thresholding sparse matching; outlier detection; genetic algorithm; epipolar geometry; evolutionary search; guided sampling; adaptive thresholding
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MDPI and ACS Style

Shahbazi, M.; Sohn, G.; Théau, J. Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection. Algorithms 2017, 10, 87. https://doi.org/10.3390/a10030087

AMA Style

Shahbazi M, Sohn G, Théau J. Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection. Algorithms. 2017; 10(3):87. https://doi.org/10.3390/a10030087

Chicago/Turabian Style

Shahbazi, Mozhdeh; Sohn, Gunho; Théau, Jérôme. 2017. "Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection" Algorithms 10, no. 3: 87. https://doi.org/10.3390/a10030087

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