Publication: Reducing the amount of input data in traffic sign classification
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2006
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Abstract
Several complex problems have to be solved in order to build Intelligent Transport Systems. Among them, it is worth mentioning the detection and classification of tra±c signs which could appear at any position within a captured image. This paper analyzes the influence of the number of attributes in the field of classification of tra±c signs when automatic learning techniques are used. In order to face this task, four different approaches have been considered, three of them symbolic and one sub-symbolic. These techniques have been applied using two different input pattern dimensions and their performances have been compared.
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Proceeding of: 3th International Conference Modeling Decisions for Artificial Intelligence, MDAI 2006. Tarragona, Catalonia, Spain, april 3-5th, 2006.
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Thrid International Conference Modeling Decisions for Artificial Intelligence, MDAI 2006