An Interval Branch and Bound Algorithm for Parameter Estimation

Bertrand Neveu 1, 2, 3 Martin De La Gorce 2, 1, 3 Gilles Trombettoni 4
3 IMAGINE [Marne-la-Vallée]
CSTB - Centre Scientifique et Technique du Bâtiment, LIGM - Laboratoire d'Informatique Gaspard-Monge, ENPC - École des Ponts ParisTech
4 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The parameter estimation problem is a widespread and challenging problem in engineering sciences consisting in computing the parameters of a parametric model that fit observed data. The computer vision community has proposed the RANSAC algorithm to deal with outliers in the observed data. This randomized algorithm is efficient but non-deterministic and therefore incomplete. Jaulin et al. propose a branch-and-contract algorithm that returns all the model instances fitting at least q observations. Assuming that at least q observed data are inliers, this algorithm achieves on the observations a relaxed intersection operator called q-intersection. First, this paper presents several improvements to Jaulin et al.'s algorithm. Second, an interval branch and bound algorithm is designed to produce a model that can explain the maximum number of observations within a given tolerance. Experiments are carried out on computer vision and image processing problems. They highlight a significant speedup w.r.t. Jaulin et al.'s interval method in 2D and 3D shape recognition problems. We have also investigated how the approach scales up in dimensions up to 7 for stereovision (estimation of essential and fundamental matrices).
Type de document :
Communication dans un congrès
GOW: Globla Optimization Workshop, Sep 2016, Braga, Portugal. 13th Globla Optimization Workshop, 2016, 〈http://apolo.dps.uminho.pt/gow16/〉
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Bertrand Neveu, Martin De La Gorce, Gilles Trombettoni. An Interval Branch and Bound Algorithm for Parameter Estimation. GOW: Globla Optimization Workshop, Sep 2016, Braga, Portugal. 13th Globla Optimization Workshop, 2016, 〈http://apolo.dps.uminho.pt/gow16/〉. 〈hal-01376601〉

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