Robust regression and outlier detection
Robust regression and outlier detection
Robust regression methods for computer vision: a review
International Journal of Computer Vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Robust Parameter Estimation in Computer Vision
SIAM Review
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Preemptive RANSAC for Live Structure and Motion Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sampling Minimal Subsets with Large Spans for Robust Estimation
International Journal of Computer Vision
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It has been observed previously that the number of iterations required to derive good model parameter values used by RANSAC-like model estimators is too optimistic. We present the derivation of an analytical formula that allows the calculation of the sufficient limit of iterations needed to obtain good parameter values with the prescribed probability for any number of model parameters. It explains the values that had been found experimentally for certain numbers of model parameters by others very well. Furthermore, the improvement that our approach of SUfficient Random SAmple Coverage (SURSAC) offers, in comparison to the original RANSAC algorithm as well as to its adaptive modification by Hartley and Zisserman, is demonstrated with synthetic data for the case of a non-linear model function over a wide range of outlier fractions and different ratios of inlier and outlier densities.