Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30183
Título : A syntactical approach to learn and identify bidimensional image models
Autor : Saínz, Miguel, Sanfeliu Cortés, Alberto
Palabras clave : Automatic learning
Generic multi-purpose model
Grammatica inference
Augmented regular expressions
Context sensitive grammars
Pattern recognition: Computer vision
Computer vision
Fecha de publicación : 1997
Editor: Asociación Española de Reconocimientos de Formas y Análisis de Imágenes
Resumen: In one hand, automatic generation of models from a set of positive and negative samples and a-priori knowledge (if available) is a crucial issue for pattern recongnition applications. In the other hand, a generic multipurpose 2D object model representation is very useful in object recognition in complex scenes. In this paper we present a new approach of 2D objects multi-purpose model model representation based in context sensitive language and automatic learning. To illustrate the model representation of the performances achieved two different applications have been developed: an outdoor traffic sign identifier and a human face identifier. Partial results of the recognition process of both applications are shown.
Descripción : Spanish Symposium on Pattern Recognition and Image Analysis (SNRFAI), 1997, Barcelona (España)
URI : http://hdl.handle.net/10261/30183
Citación : 7th Spanish Symposium on Pattern Recognition and Image Analysis: 1-3 (1997)
Appears in Collections:(IRII) Comunicaciones congresos

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