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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10016/11217
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| Title: | Reducing the amount of input data in traffic sign classification |
| Author(s): | Granados, Ana
Ledezma, Agapito
Gutiérrez, Germán
Sanchis, Araceli |
| Issued date: | 2006 |
| Citation: | Thrid International Conference Modeling Decisions for Artificial Intelligence, MDAI 2006 |
| URI: | http://hdl.handle.net/10016/11217 |
| Description: | Proceeding of: 3th International Conference Modeling Decisions for Artificial Intelligence, MDAI 2006. Tarragona, Catalonia, Spain, april 3-5th, 2006. |
| 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. |
| Sponsor: | The research reported here was carried out as a part of the research project CICYT TRA2004-07441-C03-01. |
| Appears in Collections: | DI - CAOS - Comunicaciones en Congresos y otros eventos
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