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Model Indexing: the Graph-Hashing Approach

Humberto Sossa 1, 2 Radu Horaud 1, 2 
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : The problem of object recognition in computer vision is addressed. A method for model indexing, which, given a group of image features, rapidly extracts from the list of objects those objects containing this group of features, is presented. The method operates on an abstract representation of features, more precisely, groups of features. In practice, this abstract representation takes the form of a graph. The present study deals with binary graphs only, that is, only one feature-type and one feature-relationship-type can be embedded in the representation.
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Submitted on : Tuesday, May 3, 2011 - 4:18:35 PM
Last modification on : Wednesday, May 4, 2022 - 12:12:03 PM
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Humberto Sossa, Radu Horaud. Model Indexing: the Graph-Hashing Approach. IEEE Conference on Computer Vision and Pattern Recognition (CVPR '92), Jun 1992, Urbana-Champaign, United States. pp.811--814, ⟨10.1109/CVPR.1992.223252⟩. ⟨inria-00590011⟩



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