Real-time classification of traffic signs
Real-Time Imaging
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Road Signs Recognition Using a Dynamic Pixel Aggregation Technique in the HSV Color Space
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Traffic sign recognition using colour information
Mathematical and Computer Modelling: An International Journal
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A fast method to detect and recognize scaled and skewed road signs is proposed in this paper. The input color image is first quantized in HSV color model. Border tracing those regions with the same colors as road signs is adopted to find the regions of interest (ROI). Verification is then performed to find those ROIs satisfying specific constraints as road sign candidates. The candidate regions are extracted and normalization is automatically calculated to handle scaled and skewed road signs. Finally, matching based on distance maps is adopted to measure the similarity between the scene and model road signs to accomplish recognition. Experimental results show that the proposed method is effective and efficient, even for scaled and skewed road signs in complicated scenes. On the average, it takes 4–50 and 11 ms for detection and recognition, respectively. Thus, the proposed method is adapted to be implemented in real time.