Matching and Clustering: Two Steps Toward Automatic Object Modeling in Computer Vision

Patrick Gros 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this article, we present a general frame for a system of au tomatic modeling and recognition of 3D polyhedral objects. Such a system has many applications for robotics: e.g., recog nition, localization, and grasping. Here we focus on one main aspect of the system: when many images of one 3D object are taken from different unknown viewpoints, how to recognize those that represent the same aspect of the object? Briefly, is it possible to determine automatically if two images are similar or not? The two stages detailed in the article are the matching of two images and the clustering of a set of images. Matching consists of finding the common features of two images while no information is known about the image contents, the motion, or the calibration of the camera. Clustering consists of regrouping into sets the images representing a same aspect of the modeled objects. For both stages, experimental results on real images are shown.
Type de document :
Article dans une revue
International Journal of Robotics Research, SAGE Publications, 1995, 14 (6), pp.633-642. 〈http://ijr.sagepub.com/content/14/6/633.full.pdf+html〉. 〈10.1177/027836499501400608〉
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https://hal.inria.fr/inria-00604074
Contributeur : Patrick Gros <>
Soumis le : mardi 28 juin 2011 - 10:07:53
Dernière modification le : jeudi 11 janvier 2018 - 06:20:10

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Patrick Gros. Matching and Clustering: Two Steps Toward Automatic Object Modeling in Computer Vision. International Journal of Robotics Research, SAGE Publications, 1995, 14 (6), pp.633-642. 〈http://ijr.sagepub.com/content/14/6/633.full.pdf+html〉. 〈10.1177/027836499501400608〉. 〈inria-00604074〉

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