Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Detection and classification of road signs in natural environments
Neural Computing and Applications
Fourier Preprocessing for Hand Print Character Recognition
IEEE Transactions on Computers
Fast road sign detection using hough transform for assisted driving of road vehicles
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Color fused multiple features for traffic sign recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
EDCircles: A real-time circle detector with a false detection control
Pattern Recognition
Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle
Machine Vision and Applications
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Traffic sign recognition is important for the development of driver assistance systems and fully autonomous vehicles. Even though GPS navigator systems works well for most of the time, there will always be situations when they fail. In these cases, robust vision based systems are required. Traffic signs are designed to have distinct colored fields separated by sharp boundaries. We propose to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes. The contours are described by Fourier descriptors. Matching of a query image to the sign prototype database is done by exhaustive search. This is done efficiently by using the correlation based matching scheme for Fourier descriptors and a fast cascaded matching scheme for enforcing the spatial requirements. We demonstrated on a publicly available database state of the art performance.