Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities

Joan Mas Romeu 1 Bart Lamiroy 2 Gemma Sánchez 1 Josep Lladós 1
2 QGAR - Querying Graphics through Analysis and Recognition
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper we address both automatic recognition of sketched symbols and the construction of the corresponding models from user drawn examples. Our approach is based on a two stage process. In a first phase we use an Adjacency Grammar to express topological properties of the symbol. In order to be able to further disambiguate topologically similar configurations on the rules of the grammar that are triggered by the recognition process produce a set of local geometric invariants is defined. The combination of both steps results in an efficient recognition method for user drawn sketches. Furthermore, we show that the same approach can easily be adapted for the generation of Adjacency Grammars from user provided and hand drawn examples.
Type de document :
Communication dans un congrès
3rd Eurographics Workshop on Sketch-Based Interfaces and Modeling, Sep 2006, Vienne/Autriche, 2006
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https://hal.inria.fr/inria-00104537
Contributeur : Bart Lamiroy <>
Soumis le : samedi 6 octobre 2007 - 07:00:04
Dernière modification le : jeudi 11 janvier 2018 - 06:19:59
Document(s) archivé(s) le : jeudi 20 septembre 2012 - 11:27:04

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  • HAL Id : inria-00104537, version 1

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Joan Mas Romeu, Bart Lamiroy, Gemma Sánchez, Josep Lladós. Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities. 3rd Eurographics Workshop on Sketch-Based Interfaces and Modeling, Sep 2006, Vienne/Autriche, 2006. 〈inria-00104537〉

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