Simultaneous Robust Fitting of Multiple Curves
Abstract
In this paper, we address the problem of robustly recovering several
instances of a curve model from a single noisy data set with
outliers. Using M-estimators revisited in a Lagrangian formalism, we
derive an algorithm that we call SMRF, which extends the classical
Iterative Reweighted Least Squares algorithm (IRLS). Compared to the
IRLS, it features an extra probability ratio, which is classical in
clustering algorithms, in the expression of the weights. Potential
numerical issues are tackled by banning zero probabilities in the
computation of the weights and by introducing a Gaussian prior on
curves coefficients. Applications to camera calibration and
lane-markings tracking show the effectiveness of the SMRF algorithm,
which outperforms classical Gaussian mixture model algorithms in the presence
of outliers.
Reference
@inproceedings{jpt-visapp07b,
author = {Tarel, J.-P. and Charbonnier, P. and Ieng, S.-S.},
title = {Simultaneous Robust Fitting of Multiple Curves},
booktitle = {Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP'07)},
date = {March 8-11},
address = {Barcelona, Spain},
pages = {175-182},
year = {2007},
note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/visapp07b.html}
}
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