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ADAC
2008

A constrained-optimization based half-quadratic algorithm for robustly fitting sets of linearly parametrized curves

9 years 2 days ago
A constrained-optimization based half-quadratic algorithm for robustly fitting sets of linearly parametrized curves
We consider the problem of multiple fitting of linearly parametrized curves, that arises in many computer vision problems such as road scene analysis. Data extracted from images usually contain non-Gaussian noise and outliers, which makes classical estimation methods ineffective. In this paper, we first introduce a family of robust probability density functions which appears to be well-suited to many real-world problems. Also, such noise models are suitable for defining continuation heuristics to escape shallow local minima and their robustness is devised in terms of breakdown point. Second, the usual Iterative Reweighted Least Squares (IRLS) robust estimator is extended to the problem of robustly estimating sets of linearly parametrized curves. The resulting, non convex optimization problem is tackled within a Lagrangian approach, leading to the so-called Simultaneous Robust Multiple Fitting (SRMF) algorithm, whose global convergence to a local minimum is proved using results from con...
Jean-Philippe Tarel, Sio-Song Ieng, Pierre Charbon
Added 08 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2008
Where ADAC
Authors Jean-Philippe Tarel, Sio-Song Ieng, Pierre Charbonnier
http://perso.lcpc.fr/tarel.jean-philippe/publis/adac08.html
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