Robust regression and outlier detection
Robust regression and outlier detection
Machine Learning
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Independent Motion Detection in 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation
International Journal of Computer Vision
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Multivariate Analysis
Outlier rejection in high-dimensional deformable models
Image and Vision Computing
A consensus sampling technique for fast and robust model fitting
Pattern Recognition
Bounded influence support vector regression for robust single-model estimation
IEEE Transactions on Neural Networks
Foreground/background segmentation with learned dictionary
ASMCSS'09 Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modelling, Circuits, Systems and Signals
Technical Section: Robust normal estimation for point clouds with sharp features
Computers and Graphics
Improved object tracking using an adaptive colour model
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A region-based randomized voting scheme for stereo matching
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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Two new techniques based on nonparametric estimation of probability densities are introduced which improve on the performance of equivalent robust methods currently employed in computer vision. The first technique draws from the projection pursuit paradigm in statistics, and carries out regression M-estimation with a weak dependence on the accuracy of the scale estimate. The second technique exploits the properties of the multivariate adaptive mean shift, and accomplishes the fusion of uncertain measurements arising from an unknown number of sources. As an example, the two techniques are extensively used in an algorithm for the recovery of multiple structures from heavily corrupted data.