Robust Regression and Outlier Detection with SVR: Application to Optic Flow Estimation | Zendy

Johan Colliez | Zendy; F. Dufrenois | Zendy; Denis Hamad | Zendy

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Robust Regression and Outlier Detection with SVR: Application to Optic Flow Estimation

Author(s) -

Johan Colliez,

F. Dufrenois,

Denis Hamad

Publication year - 2006

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.20.125

Subject(s) - outlier , robustness (evolution) , computation , support vector machine , robust regression , computer science , artificial intelligence , anomaly detection , regression , pattern recognition (psychology) , regression analysis , robust statistics , optical flow , algorithm , machine learning , image (mathematics) , mathematics , statistics , biochemistry , chemistry , gene

The robust regression is an important tool for the analysis of data contamined by outliers. In computer vision, the optic flow computation is considered as belonging to this kind of problem. In this paper, we discuss a robust optic flow computation based on a modified support vector regression (SVR) technique. We experimentally show that the proposed method significantly improves the robustness against outliers compared to traditional SVR. Next, we illustrate the performances of the method for the optic flow computation problem from noised image sequences.

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