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
Optic flow estimation by support vector regression
Engineering Applications of Artificial Intelligence
A kernel hat matrix based rejection criterion for outlier removal in support vector regression
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Bounded influence support vector regression for robust single-model estimation
IEEE Transactions on Neural Networks
Contextual soccer detection using mosaicing techniques
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
| Hi-index | 0.00 |
Robust estimators, such as Least Median of Squared (LMedS)Residuals, M-estimators, the Least Trimmed Squares (LTS) etc., havebeen employed to estimate optical flow from image sequences inrecent years. However, these robust estimators have a breakdownpoint of no more than 50%. In this paper, we propose a novel robustestimator, called variable bandwidth Quick Maximum Density PowerEstimator (vbQMDPE),which can tolerate more than 50% outliers. Weapply the novel proposed estimator to robust optical flowestimation. Our method yields better results than most otherrecently proposed methods, and it has the potential to betterhandle multiple motion effects.