A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust Adaptive Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ROR: Rejection of Outliers by Rotations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Robust Estimation for Range Image Segmentation and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Matching constraints and the joint image
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
International Journal of Computer Vision
MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Modified pbM-Estimator Method and a Runtime Analysis Technique for the RANSAC Family
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Moving object segmentation using motor signals
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Epipolar geometry estimation for urban scenes with repetitive structures
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Regularized vector field learning with sparse approximation for mismatch removal
Pattern Recognition
ReigSAC: fast discrimination of spurious keypoint correspondences on planar surfaces
Machine Vision and Applications
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The problem of automatic robust estimation of the epipolar geometry in cases where the correspondences are contaminated with a high percentage of outliers is addressed. This situation often occurs when the images have undergone a significant deformation, either due to large rotation or wide baseline of the cameras. An accelerated algorithm for the identification of the false matches between the views is presented. The algorithm generates a set of weak motion models (WMMs). Each WMM roughly approximates the motion of correspondences from one image to the other. The algorithm represents the distribution of the median of the geometric distances of a correspondence to the WMMs as a mixture model of outlier correspondences and inlier correspondences. The algorithm generates a sample of outlier correspondences from the data. This sample is used to estimate the outlier rate and to estimate the outlier pdf. Using these two pdfs the probability that each correspondence is an inlier is estimated. These probabilities enable guided sampling. In the RANSAC process this guided sampling accelerates the search process. The resulting algorithm when tested on real images achieves a speedup of between one or two orders of magnitude.