Mesure de Similarité Entre Sous-parties de nuages de points 2D

Mesure de Similarité Entre Sous-parties de nuages de points 2D

Christophe Palmann Sébastien Mavromatis Jean Sequeira 

Projet SimGraph – LSIS – UMR CNRS 6168 ESIL – Case 925, 163 av. de Luminy, F-13228 Marseille cedex 9

Corresponding Author Email: 
christophe.palmann@gmail.com
Page: 
29-49
|
DOI: 
https://doi.org/10.3166/ts.29.29-49
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

This communication focuses on the characterisation of a similarity measure between parts of 2D point clouds. This measure is defined thanks to the use of a general knowledge about real point clouds: they share a large amount of one-dimensional structures. These structures can be represented into a unified manner with a new type of primitives; then, we set the link between the existence of common information between parts of point clouds and the geometric relations of their primitives. Thus, we define a similarity measure that is rotationally invariant, and an algorithm to compute it. 

RÉSUMÉ

Cet article porte sur la caractérisation d’une mesure de similarité entre sous-parties de nuages de points 2D. Cette mesure est définie à partir d’une hypothèse généralement vérifiée sur des nuages de points issus de cas réels : ceux-ci possèdent des groupes de points qui s’organisent en structures linéiques, et qui apparaissent en même temps dans les différents nuages. Après avoir défini des primitives qui permettent une représentation unifiée de ces structures, nous montrons le lien qui existe entre la présence d’une information commune entre sous-nuages et la distribution des relations géométriques entre leurs primitives. Nous donnons alors une mesure de similarité invariante par rotation, ainsi qu’un algorithme permettant de la calculer.

Keywords: 

point clouds, measure of similarity, registration process, pattern recognition

MOTS-CLÉS

nuages de points, mesure de similarité, recalage, reconnaissance de formes​

Extended abstract
1. Introduction
2. État de l’art
3. Modélisation de structures linéiques par de nouvelles primitives
4. Distribution des différences d’orientations
5. Définition et mise en oeuvre d’une mesure de similarité entre nuages de points
6. Application à l’identification de régions similaires dans des images de modalités différentes
7. Conclusion
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