ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Using self-organising maps in the detection and recognition of road signs
Image and Vision Computing
Robust class similarity measure for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
Using fourier descriptors and spatial models for traffic sign recognition
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition
Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
Neural network based smart vision system for driver assistance in extracting traffic signposts
Proceedings of the CUBE International Information Technology Conference
Real-Time GPU based road sign detection and classification
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Symbol recognition in natural scenes by shape matching across multi-scale segmentations
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Real-time traffic sign recognition in three stages
Robotics and Autonomous Systems
Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle
Machine Vision and Applications
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An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.