The act of distributing things into classes or categories of the same type.
Published in Chapter:
Intelligent Traffic Sign Classifiers
Raúl Vicen Bueno (University of Alcalá, Spain), Elena Torijano Gordo (University of Alcalá, Spain), Antonio García González (University of Alcalá, Spain), Manuel Rosa Zurera (University of Alcalá, Spain), and Roberto Gil Pita (University of Alcalá, Spain)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch141
Abstract
The Artificial Neural Networks (ANNs) are based on the behavior of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between different kinds of traffic signs. Moreover, this ANN learning must be done for traffic signs that are not in perfect conditions. So, the learning must be robust against several problems like rotation, translation or even vandalism. In order to achieve this objective, an intelligent extraction of information from the images is done. This stage is very important because it improves the performance of the ANN in this task.