Title :
A neural network based automatic road signs recognizer
Author :
Vitabile, S. ; Gentile, A. ; Sorbello, F.
Author_Institution :
CEntro di studio sulle Reti di Elaboratori, Palermo, Italy
fDate :
6/24/1905 12:00:00 AM
Abstract :
Automatic road sign recognition systems are aimed at detection and recognition of one or more road signs from real-world color images. In this research, road signs are detected and extracted from real world scenes on the basis of their color and shape features. A dynamic region growing technique is adopted to enhance color segmentation results obtained in the HSV color space. The technique is based on a dynamic threshold that reduces the effect of hue instability in real scenes due to external brightness variation. Classification is then performed on extracted candidate regions using multilayer perceptron neural networks. The obtained results show good detection and recognition rates of the entire system with real outdoor scenes, using several light conditions. Finally, the implementation of the neural layer on the Georgia Institute of Technology SIMD Pixel Processor is outlined
Keywords :
image colour analysis; image recognition; image segmentation; multilayer perceptrons; object recognition; HSV color space; SIMD Pixel Processor; classification; color features; color segmentation enhancement; dynamic region growing technique; dynamic threshold; external brightness variation; hue instability; multilayer perceptron neural networks; neural network based automatic road signs recognizer; real-world color images; shape features; Brightness; Color; Image recognition; Image segmentation; Layout; Multi-layer neural network; Multilayer perceptrons; Neural networks; Roads; Shape;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007503