Object detection in multi-channel and multi-scale images based on the structural tensor
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Automatic detection and recognition of signs from natural scenes
IEEE Transactions on Image Processing
Intelligent System for Traffic Signs Recognition in Moving Vehicles
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Real-Time Road Signs Tracking with the Fuzzy Continuously Adaptive Mean Shift Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Road-signs recognition system for intelligent vehicles
RobVis'08 Proceedings of the 2nd international conference on Robot vision
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The paper presents a system for the road signs recognition which is based on an ensemble of the non Euclidean distance neural networks and an arbitration unit. The input to this system constitutes a binary pictogram of a sign which is supplied from the detection module. The classifier is composed of a mixture of experts – the Hamming neural networks – each working with a single group of deformed reference pictograms. The ensemble of experts is controlled by an arbitration module operating in the winner-takes-all mode. Additionally it is equipped with a promoting mechanism that favours the most populated group of unanimous experts. The presented classifier is characterized by the fast training and very fast response times which features make it suitable for the driving assistant systems. The presented concepts have been verified experimentally. Their results and conclusions are also discussed.