The Likelihood-Ratio Test and Efficient Robust Estimation

Andrea Cohen, Christopher Zach; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2282-2290

Abstract


Robust estimation of model parameters in the presence of outliers is a key problem in computer vision. RANSAC inspired techniques are widely used in this context, although their application might be limited due to the need of a priori knowledge on the inlier noise level. We propose a new approach for jointly optimizing over model parameters and the inlier noise level based on the likelihood ratio test. This allows control over the type I error incurred. We also propose an early bailout strategy for efficiency. Tests on both synthetic and real data show that our method outperforms the state-of-the-art in a fraction of the time.

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[bibtex]
@InProceedings{Cohen_2015_ICCV,
author = {Cohen, Andrea and Zach, Christopher},
title = {The Likelihood-Ratio Test and Efficient Robust Estimation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}