Object Recognition: Solution of the Simultaneous Pose and Correspondence Problem. Reconnaissance D’Objets Volumiques par Mise en Correspondance D’Indices Visuels

Object Recognition: Solution of the Simultaneous Pose and Correspondence Problem

Reconnaissance D' Objets Volumiques par Mise en Correspondance D' Indices Visuels

Frédéric Jurie

LASMEA – UMR 6602 du CNRS, Campus Universitaire des Cézeaux, 63177 Aubière Cedex

Page: 
321-344
 |
Received: 
2 October 2000
| |
Accepted: 
N/A
| | Citation
ts18_5-6_321-344.pdf

OPEN ACCESS

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 multi resolution 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.

Résumé

Nous nous intéressons à la reconnaissance d’objets volumiques par mise en correspondance d’indices visuels. Nous supposons que les objets à reconnaître sont représentés à l’aide de modèles tridimensionnels, composés d’indices visuels. Reconnaître un objet signifie, dans ce cas, mettre en correspondance les indices du modèle de cet objet avec des indices extraits de l’image, de manière à ce que ces derniers puissent s’expliquer comme une transformation géométrique des indices du modèle. La recherche de la pose (valeur des paramètres de la transformation alignant le modèle sur l’image) et la recherche des correspondances sont ici traitées simultanément. Cela constitue l’originalité et la force de la méthode que nous proposons. Nous présentons de nombreux résultats expérimentaux illustrant l’utilisation de notre approche pour la reconnaissance d’objets.

Keywords: 

Model-based recognition; pose verificatio

Mots clés 

Reconnaissance d’objets, mise en correspondance

1. Introduction
2. Vers une Technique Efficace pour la Mise en Correspondance
3. Exploration Récursive de L’Espace des Poses
4. Probabilité qu’un Modèle d’Objet Soit dans l’Image, pour une Pose Donnée, et pour une Boîte Donnée.
5. Probabilité d’un Appariement Primitive Modèle – Primitive Image Connaissant une Pose p P(M|p)
6. Probabilité d’une Correspondance de Primitive Image à Primitive Modèle pour une Boîte Donnée (P(C|BOX))
7. Application à la Reconnaissance d’Objets par Utilisation de Collections de Vues 2D
8. Reconnaissance par Utilisation de Modèles 3D
9. Comparaison avec Des Travaux Antérieurs
10. Conclusions
  References

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