A Constrained-Optimization based Half-Quadratic Algorithm for Robustly Fitting Sets of Linearly Parametrized Curves - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Advances in Data Analysis and Classification Année : 2008

A Constrained-Optimization based Half-Quadratic Algorithm for Robustly Fitting Sets of Linearly Parametrized Curves

Résumé

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 constrained optimization theory.
Fichier non déposé

Dates et versions

hal-00402846 , version 1 (08-07-2009)

Identifiants

  • HAL Id : hal-00402846 , version 1

Citer

Jean Philippe Tarel, Sio Song Ieng, Pierre Charbonnier. A Constrained-Optimization based Half-Quadratic Algorithm for Robustly Fitting Sets of Linearly Parametrized Curves. Advances in Data Analysis and Classification, 2008, 2 (3), pp 227-239. ⟨hal-00402846⟩
53 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More