Neural Networks
Computational strategies for object recognition
ACM Computing Surveys (CSUR)
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Traffic Sign Recognition Revisited
Mustererkennung 1999, 21. DAGM-Symposium
Fast object recognition in noisy images using simulated annealing
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
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Traffic signs can provide drivers with very valuable information about the road, in order to make driving safer and easier. In recent years, traffic signs recognition has aroused wide interests in many scholars. It has two main parts– the detection and the classification. This paper presents a new method for traffic signs classification based on probabilistic neural networks (PNN) and Tchebichef moment invariants. It has two hierarchies: the first hierarchy classifier can coarsely classify the input image into one of indicative signs, warning signs or prohibitive signs according to its background color threshold; the second hierarchy classifiers including of three PNN networks can concretely identify traffic sign. The inputs of every PNN use the new developed Tchebichef moment invariants. The simulation results show that the two-hierarchy classifier can improve the classification ability meanwhile can use in real-time system.