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extracted by central projection transformation is a global feature 
and invariant to object scales and rotations. Moreover, before 
the central projection transformation, detected traffic signs are 
not required to be normalized to the uniform size. Especially, 
the problem of confirming optimal projection number in central 
projection transformation is solved by the information entropy 
in this paper. Therefore, the proposed traffic sign recognition 
method in this paper is simple, reliable and high-speed for 
traffic sign recognition in natural scene images. 
REFERENCES 
1. Alefs, B., Eschemann, G., Ramoser, EL, et al, 2007. Road 
sign detection from edge orientation histogramms. In: 
Proceedings of the 2007 IEEE Intelligent Vehicles 
Symposium, Istanbul, Turkey, pp. 993-998. 
2. Escalera, A. de la., Armingol, J. M., Mata, M., 2003. 
Traffic sign recognition and analysis for intelligent vehicles. 
Image and Vision Computing, 21(3), pp. 247-258. 
3. Fang, C. Y„ Fuh, C. S„ Yen, P. S., et al, 2004. An 
automatic road sign recognition system based on a 
computational model of human recognition processing. 
Computer Vision and Image Understanding, 96(2), pp. 237- 
268. 
4. Filipe, C., Correia, P. L., 2007. Automatic detection and 
classification of traffic signs. In: Proceedings of IEEE 
Eighth International Workshop on Image Analysis for 
Multimedia Interactive Services, Santorini, Greece, pp. 8-11. 
5. Fleyeh, H., Dougherty, M., Aenugula, D., et al, 2007. 
Invariant road sign recognition with fuzzy ARTMAP and 
Zemike moments. In: Proceedings of the 2007 IEEE 
Intelligent Vehicles Symposium, Istanbul, Turkey, pp. 31-36. 
6. Gao, X. W., Podladchikova, L., Shaposhnikov, D., et al, 
2006. Recognition of traffic signs based on their colour and 
shape features extracted using human vision models. Journal 
of Visual Communication & Image Representation, 17(4), pp. 
675-685. 
7. Gil-Jimenez, P., Gomez-Moreno, H., Siegmann, P., et al, 
2007. Traffic sign shape classification based on support 
vector machines and the FFT of the signature of blobs. In: 
Proceedings of the 2007 IEEE Intelligent Vehicles 
Symposium, Istanbul, Turkey, pp. 375-380. 
8. Hsu, S. H., Huangl, C. L., 2001. Road sign detection and 
recognition using matching pursuit method. Image and 
Vision Computing, 19(3), pp. 119-129. 
9. Hu, M. K., 1962. Visual pattern recognition by moment 
invariant. IEEE Trans. On Information Theory, 28(8), pp. 
179-187. 
10. Kuo, W. J., Lin, C. C., 2007. Two-stage road sign detection 
and recognition. In: Proceedings of2007 IEEE International 
Conference on Multimedia and Expo, Beijing, China, pp. 
1427-1430. 
11. Li, Q. H., Zhang, H., 2006. A modified PNN-based method 
for traffic signs classification. Systems Engineering, 24(4), 
pp. 97-101. 
12. Liu, H., Liu, D., Xin, J., 2002. Real-time recognition of 
road traffic sign in motion image based on genetic algorithm. 
In: Proceedings of the 1st International Conference on 
Machine Learning and Cybernetics, Beijing, China, pp. 83- 
86. 
13. Loy, G., Barnes, N., 2004. Fast shape-based road sign 
detection for a driver assistance system. In: Proceedings of 
International Conference on Intelligent Robots and Systems, 
Sendai, Japan, pp. 70-75. 
14. Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, 
P., et al, 2007. Road-sign detection and recognition based on 
support vector machines. IEEE Transactions on Intelligent 
Transportation System, 8(2), pp. 264-278. 
15. Miura, J., Kanda, T., Shirai, Y., 2000. An active vision 
system for real-time traffic sign recognition. In: Proceedings 
of IEEE Intelligent Transportation Systems, Dearborn, USA, 
pp. 52-57. 
16. Paclik, P., Novovicova, J., Duin, R. P. W., 2006. Building 
road-sign classifiers using a trainable similarity measure. 
IEEE Transactions on Intelligent Transportation System, 
7(3), pp. 309-321. 
17. Perez, E., Javidi, B., 2002. Nonlinear distortion-tolerant 
filters for detection of road signs in background noise. IEEE 
Transactions on Vehicular Technology, 51(3), pp. 567-576. 
18. Specht, D. F., 1990. Probabilistic neural networks and 
polynomial adaline as complementary techniques for 
classification. IEEE Transactions on Neural Network, 1(1), 
pp. 111-121. 
19. Tao, Y., Lam, E. C. M., Tang, Y. Y., 2001. Feature 
extraction using wavelet and fractal. Pattern Recognition 
Letters, 22(3-4), pp. 271-287. 
20. Zhang, K., Sheng, Y. H., Gong, Zh. J., et al, 2007. A self- 
adaptive algorithm for traffic sign detection in motion image 
based on colour and shape features. In: Proceedings of SPIE, 
Geoinformatics 2007: Remotely Sensed Data and 
Information, Nanjing, China, 6752, pp. 6752G1-6752G13. 
ACKNOWLEDGES 
This research is supported by National Foundation of Sciences 
under the grant number 40671147, and Momentous Science 
Foundation of Jiangsu province under the grant number 
07KJA42005.