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Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30183
Title: A syntactical approach to learn and identify bidimensional image models
Authors: Saínz, Miguel; Sanfeliu Cortés, Alberto
Keywords: Automatic learning
Generic multi-purpose model
Grammatica inference
Augmented regular expressions
Context sensitive grammars
Pattern recognition: Computer vision
Computer vision
Issue Date: 1997
Publisher: Asociación Española de Reconocimientos de Formas y Análisis de Imágenes
Citation: 7th Spanish Symposium on Pattern Recognition and Image Analysis: 1-3 (1997)
Abstract: 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.
Description: Spanish Symposium on Pattern Recognition and Image Analysis (SNRFAI), 1997, Barcelona (España)
URI: http://hdl.handle.net/10261/30183
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