Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Circular road signs recognition with soft classifiers
Integrated Computer-Aided Engineering - Artificial Neural Networks
Rotation invariant recognition of road signs with ensemble of 1-NN neural classifiers
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Recognition of road signs with mixture of neural networks and arbitration modules
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
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
A Real-Time Vision System for Traffic Signs Recognition Invariant to Translation, Rotation and Scale
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Road-signs recognition system for intelligent vehicles
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Traffic scene segmentation and robust filtering for road signs recognition
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Triangular traffic signs detection based on RSLD algorithm
Machine Vision and Applications
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Road signs recognition systems are developed to assist drivers and to help increase traffic safety. Shape detectors constitute a front-end in majority of such systems. In this paper we propose a method for robust detection of triangular, rectangular and rhombus shaped road signs in real traffic scenes. It starts with segmentation of colour images. For this purpose the histograms were created from hundreds of real warning and information signs. Then the characteristic points are detected by means of the developed symmetrical detector of local binary features. The points are further clusterized and used to select shapes from the input images. Finally, the shapes are verified to fulfil geometrical properties defined for the road signs. The proposed detector shows high accuracy and very fast operation time what was verified experimentally.