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

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.

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