Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error Model

Frédéric Jurie 1
1 image
LASMEA - Laboratoire des sciences et matériaux pour l'électronique et d'automatique
Abstract : The use of hypothesis verification is recurrent in the model-based recognition literature. Verification consists in measuring how many model features transformed by a pose coincide with some image features. When data involved in the computation of the pose are noisy, the pose is inaccurate and difficult to verify, especially when the objects are partially occluded. To address this problem, the noise in image features is modeled by a Gaussian distribution. A probabilistic framework allows the evaluation of the probability of a matching, knowing that the pose belongs to a rectangular volume of the pose space. It involves quadratic programming, if the transformation is affine. This matching probability is used in an algorithm computing the best pose. It consists in a recursive multiresolution exploration of the pose space, discarding outliers in the match data while the search is progressing. Numerous experimental results are described. They consist of 2D and 3D recognition experiments using the proposed algorithm.
Type de document :
Article dans une revue
Computer Vision and Image Understanding, Elsevier, 1999, 73 (3), pp.357--373. 〈10.1006/cviu.1998.0735〉
Liste complète des métadonnées

https://hal.inria.fr/inria-00548323
Contributeur : Thoth Team <>
Soumis le : lundi 20 décembre 2010 - 08:43:20
Dernière modification le : mardi 5 juin 2018 - 18:00:02

Lien texte intégral

Identifiants

Citation

Frédéric Jurie. Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error Model. Computer Vision and Image Understanding, Elsevier, 1999, 73 (3), pp.357--373. 〈10.1006/cviu.1998.0735〉. 〈inria-00548323〉

Partager

Métriques

Consultations de la notice

57