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Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture

by Ayaou Tarik, Mourad Boussaid, Afdel Karim, Amghar Abdelah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 9
Year of Publication: 2013
Authors: Ayaou Tarik, Mourad Boussaid, Afdel Karim, Amghar Abdelah
10.5120/12767-8795

Ayaou Tarik, Mourad Boussaid, Afdel Karim, Amghar Abdelah . Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture. International Journal of Computer Applications. 73, 9 ( July 2013), 5-9. DOI=10.5120/12767-8795

@article{ 10.5120/12767-8795,
author = { Ayaou Tarik, Mourad Boussaid, Afdel Karim, Amghar Abdelah },
title = { Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 9 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number9/12767-8795/ },
doi = { 10.5120/12767-8795 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:36.417891+05:30
%A Ayaou Tarik
%A Mourad Boussaid
%A Afdel Karim
%A Amghar Abdelah
%T Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 9
%P 5-9
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the large uses of the intelligent systems in different domains, and in order to increase the drivers and pedestrians' safety, the road and traffic sign recognition system has been a challenging issue and an important task for many years. But studies, done in this field of detection and recognition of traffic signs in an image, which are interested in the Arab context, are still insufficient. Detection of the road signs present in the scene is the one of the main stages of the traffic sign detection and recognition. In this paper, an efficient solution to enhance road signs detection, including Arabic context, performance based on color segmentation, Randomized Hough Transform and the combination of Zernike moments and Haralick features has been made. Segmentation stage is useful to determine the Region of Interest (ROI) in the image. The Randomized Hough Transform (RHT) is used to detect the circular and octagonal shapes. This stage is improved by the extraction of the Haralick features and Zernike moments. Furthermore, we use it as input of a classifier based on SVM. Experimental results show that the proposed approach allows us to perform the measurement's precision.

References
  1. S. Maldonado Bascón, J. Acevedo Rodríguez, S. Lafuente Arroyo, A. Fernndez Caballero, F. López-Ferreras. An optimization on pictogram identification for the road-sign recognition task using SVMs. Computer Vision and Image Understanding 114 (2010) 373–383.
  2. Rachid Belaroussi and Jean-Philippe Tarel, 2010, Détection des panneaux de signalisation routière par accumulation bivariée, Traitement du signal. Volume 27–n? 3/2010, pages 265 à 297.
  3. A. de la Escalera*, J. Ma Armingol, M. Mata. , Traf?c sign recognition and analysis for intelligent vehicles, Image and Vision Computing 21 (2003) 247–258
  4. G. Piccioli, E. De Micheli, P. Parodi, M. Campani, A robust method for road sign detection and recognition, Image and Vision Computing 14 (3) (1996) 209–223
  5. X. W. Gao et al. , Recognition of tra?c signs based on their color and shape features extracted using human vision models. J. Vis. Commun. ImageR. 17 (2006)675–685.
  6. L. -W. Tsai et al. , Road sign detection using eigen color. IET Computer Vision 10. 1049/iet-cvi:20070058.
  7. C. F. Paulo and P. L. Correia. "Automatic Detection and Classification of Traffic Signs". Eighth International Workshop on Image Analysis for Multimedia Int, Services, Greece, 2007
  8. F. Zaklouta, B. Stanciulescu, Real-time traffic sign recognition in three stages, Robotics and Autonomous Systems (2012), doi:10. 1016/j. robot. 2012. 07. 019
  9. Andrzej Ruta et al. , Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recognition 43(2010)416—430.
  10. Shaposhnikov D. , Lubov N. , Podladchikova L. , Golovan A. , Shevtsova N. , Hong K. , Gao X. , «Road Sign Recognition by Single Positioning of Space-Variant Sensor Window», Proceedings of 15th International Conference on Vision Interface, Calgary, Canada, p. 213-217,2002.
  11. Dutilleux G. , Charbonnier P. , «Métaheuristiques biologiques pour la détection de la signalisation routière», in P. Siarry (ed. ), Optimisation en traitement du signal et de l'image,Traité IC2, série traitement du signal et de l'image, Hermes, chapter 10, p. 271-294, février, 2007.
  12. Garcia-Garrido M. , Sotelo M. , Martin-Gorostiza E. , «Fast traffic sign detection and recognition under changing lighting conditions», Proceedings of IEEE Intelligent Transportation Systems Conference (ITSC'06), Toronto, Canada, p. 811-816, 2006.
  13. Fleyeh, H. , Dougherty, M. , Aenugula, D. , Baddam, S. , Invariant Road Sign Recognition with Fuzzy ARTMAP and Zernike Moments. Intelligent Vehicles Symposium, 2007 IEEE
  14. A. V. Reina, R. J. L. Sastre, S. L. Arroyo and P. G. Jimenez. "Adaptive traffic road sign panels text extraction". Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation, Madrid, Spain, 2006
  15. Usman Zakir, Iffat Zafar & Eran A. Edirisinghe. , Road Sign Detection and Recognition by Using Local Energy based Shape Histogram (LESH). International Journal of Image Processing, (IJIP), Volume (4): Issue (6)
  16. S. Maldonado Bascón, J. AcevedoRodríguez, S. Lafuente Arroyo, A. Fernndez Caballero, F. López- Ferreras. , An optimization on pictogram identification for the road-sign recognition task using SVMs, Pattern Recognition 114 (2010) 373-383.
  17. D. Judd, D. Mac Adam, G. Wyszecki, Spectral distribution of typical daylight as a function of correlated color temperature, J. Opt. Soc. Am. 54 (8) (1964) 1031–1040.
  18. Lei Xu, Erkki Oja and Pekka Kultanen, A new curve detection methode: Randomized Hough Transform (RHT), Pattern Recognition Letters, vol. 11, no. 5, pp. 331-338, 1990.
  19. H. K. Yuen, j illingworth, and j. ittler, Detection partially occluded ellipses usin the Hough transform, Image and Vision Computing, vol. 7, no. 1, pp. 31-37, feb 1989.
  20. Bishop, C. , Pattern recognition and machine learning. New York: Springer, 2006.
  21. Robert M. Haralick, Statistical and structural approaches to texture, Proc. IEEE, vol. 67, no. 5, pp. 786-804, 1979.
  22. Chandan singh and Ekta Walia, Algorithm for fast computation of Zernike moments and their numerical stability, Image and Vision Computing 29 (2011) 251-259.
  23. Vapnik V. , Statistical Learning Theory, Springer-Verlag, New York, 1995.
  24. Huimin Qian, Yaobin Mao, Wenbo Xiang, Zhiquan Wang, Recognition of human activities using SVM multi-class classifier, Pattern Recognition Letters 31 (2010) 100-111.
Index Terms

Computer Science
Information Sciences

Keywords

Road Sign Detection Color Segmentation Randomized Hough Transform Haralick features Zernike Moments SVM classifier

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