Abstract
Automatic road sign recognition systems require a great computational cost since the number of different signs in each country is quite large. In many real-world applications only a reduced subset of traffic signs is considered in the recognition stage to verify the success of a classification algorithm. This paper proposes a optimization in the traffic sign identification task working in the spatial domain. This purpose is overcome through dimension reduction methods, such as 2DPCA and (2D)2PCA, to perform principal component analysis of training and test image vectors. The applications of these advances, using SVMs as classification technique, are shown here over a considerable database.
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Lafuente-Arroyo, S., Sánchez-Fernández, A., Maldonado-Bascón, S., Gil-Jiménez, P., Acevedo-Rodríguez, F.J. (2007). Road Sign Recognition Using Spatial Dimension Reduction Methods Based on PCA and SVMs. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_87
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DOI: https://doi.org/10.1007/978-3-540-73007-1_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
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