Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
Designing neural networks for tackling hard classification problems
WSEAS TRANSACTIONS on SYSTEMS
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
Traffic sign classification by image preprocessing and neural networks
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A Note on a priori Estimations of Classification Circuit Complexity
Fundamenta Informaticae - Hardest Boolean Functions and O.B. Lupanov
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The work presented in this paper suggests a Traffic Sign Recognition (TSR) system whose core is based on a Multilayer Perceptron (MLP). A pre-processing of the traffic sign image (blob) is applied before the core. This operation is made to reduce the redundancy contained in the blob, to reduce the computational cost of the core and to improve its performance. For comparison purposes, the performance of the a statistical method like the k-Nearest Neighbour (k-NN) is included. The number of hidden neurons of the MLP is studied to obtain the value that minimizes the total classification error rate. Once obtained the best network size, the results of the experiments with this parameter show that the MLP achieves a total error probability of 3.85%, which is almost the half of the best obtained with the k-NN.