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dc.contributor.authorMas, Juanen_US
dc.contributor.authorLamiroy, Barten_US
dc.contributor.authorSanchez, Gemmaen_US
dc.contributor.authorLlados, Josepen_US
dc.contributor.editorThomas Stahovich and Mario Costa Sousaen_US
dc.date.accessioned2014-01-27T19:17:06Z
dc.date.available2014-01-27T19:17:06Z
dc.date.issued2006en_US
dc.identifier.isbn3-905673-39-8en_US
dc.identifier.issn1812-3503en_US
dc.identifier.urihttp://dx.doi.org/10.2312/SBM/SBM06/027-034en_US
dc.description.abstractIn 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.5.1 [Computer Graphics]: Structural Model Generation based on sample users I.5.5 [Interactive systems]: Pen-Based Interfaces.en_US
dc.titleAutomatic Learning of Symbol Descriptions Avoiding Topological Ambiguitiesen_US
dc.description.seriesinformationEurographics Workshop on Sketch-Based Interfaces and Modelingen_US


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