Abstract
An Automatic Road Sign Recognition System A(RS) 2 is aimed at detection and recognition of one or more road signs from real-world color images. The authors have proposed an A(RS) 2 able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology SIMD Pixel Processor. Experimental trials supporting the feasibility of real-time processing on this platform are also reported.
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References
F. Sorbello, G. Gioiello, and S. Vitabile. Handwritten Character Recognition using a MLP, chapter 5, pages 91–119. L. C. Jain and B. Lazzerini-CRC Press, 1999.
S. Vitabile, G. Pilato, G. Pollaccia, F. Sorbello. Road Signs Recognition Using a Dynamic Pixel Aggregation Technique in the HSV Color Space. In Proc. of 11 ? International Conference on Image Analysis and Processing, Palermo-Italy, pp. 572–577, (2001), IEEE Computer Society Press.
S. Vitabile, A. Gentile, F. Sorbello. A Neural Network based Automatic Road Signs Recognizer. Proc. of 2002 IEEE World Congress on Computational Intelligence-International Joint Conference on Neural Networks (IJCNN), Honolulu-USA, pp. 2315–2320, IEEE Computer Society Press.
A. Gentile, J. Cruz-Rivera, D. Wills et al. Real-time image processing on a focal plane simd array, in parallel and distributed processing. Lecture Notes in Computer Science, (1586):400–405, 1999. Eds. J. Rolim et al.-Springer Verlag.
A. Gentile, H.H. Cat, F. Kossentini, F. Sorbello, D.S. Wills. Real-Time Vector Quantization-based Image Compression on the SIMPil Low Memory SIMD Architecture. Proc. of the 1997 IEEE Intl. Performance, Computing, and Communications Conference (IPCCC-97), pp. 10–16, 1997.
H. Akatsuka and S. Imai. Road signposts recognition system. In Proc. SAE vehicle highway infrastructure: safety compatibility, pages 189–196, 1987.
N. Kehtarnavaz, N. Griswold, and D. Kang. Stop-sign recognition based on color shape processing. In Machine Vision and Applications, volume 6, pages 206–208, 1993.
L. Priese, J. Klieber, R. Lakmann, V. Rehrmann, and R. Schian. New results on traffic sign recognition. In IEEE Proc. Intelligent Vehicles’ 94 Symposium, pages 249–253, 1994.
L. Priese and V. Rehrmann. On hierarchical color segmentation and applications. In Proc. CVPR, pages 633–634, 1993.
G. Piccioli, E. D. Michelli, and M. Campani. A robust method for road sign detection and recognition. In Proc. European Conference on Computer Vision, pages 495–500, 1994.
G. Piccioli, E. D. Michelli, P. Parodi, and M. Campani. Robust road sign detection and recognition from image sequences. In Proc. Intelligent Vehicles’94, pages 278–283, 1994.
G. Nicchiotti, E. Ottaviani, P. Castello, and G. Piccioli. Automatic road sign detection and classification from color image sequences. In S. Impedovo, editor, Proc. 7th Int. Conf. On Image Analysis and Processing, pages 623–626, 1994.
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Vitabile, S., Gentile, A., Dammone, G.B., Sorbello, F. (2002). MLP Neural Network Implementation on a SIMD Architecture. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_10
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DOI: https://doi.org/10.1007/3-540-45808-5_10
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