Road signs recognition with two-dimensional hidden Markov models

• Autorzy: Bobulski J. , Piech H.

Warianty tytułu

PL

Rozpoznawanie znaków drogowych z dwuwymiarowymi ukrytymi modelami Markowa

• Języki publikacji: EN

Abstrakty

EN

The automatic road sign recognition system is presented. The system uses two-dimensional hidden Markov models. The system is able to recognize the road signs, which were detected earlier in the image. The system uses wavelet transform for features extraction of road signs. In recognition process system uses two dimensional hidden Markov models. The experimental results demonstrate that the system is able to gain an average recognition rate of 83%.

PL

Zaprezentowano automatyczny system rozpoznawania znaków drogowych. System wykorzystuje dwuwymiarowe uryte modele Markowa. System rozponaje znaki drogowe, które były wcześniej wykryte nas obrazie. Do ekstrakcji cech znaków drogowych system używa transformaty falkowej. W procesie rozpoznawania zastosowano dwuwymiarowe modele Markowa. Wyniki eksperymentu pokazują, że system jest w stanie osiągnąć poziom rozpoznania 83%.

Słowa kluczowe

EN

two-dimensional hidden Markov model; road sign recognition; image recognition; image processing

PL

model Markova ukryty dwuwymiarowy; rozpoznawanie znaków drogowych; rozpoznawanie obrazów; przetwarzanie obrazów

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2015
• Tom: R. 91, nr 2
• Strony: 123--126
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Bobulski J.

• Institute of Computer and Information Science, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 73 Dabrowskiego Str., 42-200 Czestochowa, Poland

autor

Piech H.

• Institute of Computer and Information Science, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 73 Dabrowskiego Str., 42-200 Czestochowa, Poland

Bibliografia

• [1] Gomez-Moreno H., Lopez-Ferreras F.: Road-sign detection and recognition based on support vector machines, IEEE Transaction on Intelligent Transportation Systems, 8(2), pp.264–278, 2007.
• [2] Pazhoumand-dar H., Yaghoobi M.: A new approach in road sign recognition based on fast fractal coding, Neural Computing & Application, 22, pp.615–625, 2013.
• [3] Piccioli G., Micheli E.D., Parodi P., Campani M.: Robust method for road sign detection and recognition, Image Vision and Computing, 14, pp.209–223, 1996.
• [4] Smorawa D., Kubanek M.: Analysis of advanced techniques of image processing based on automatic detection system and road signs recognition. Journal of Applied Mathematics and Computational Mechanics, 13(1), pp.103-113 ,2014.
• [5] de la Escalera A., Moreno L., Salichs M.A., Armingol J.M.: Road traffic sign detection and classification, IEEE Transaction Industrial Electronics 44(6), pp.848–859, 1997.
• [6] Maldonado-Bascón S., Acevedo-Rodríguez J., Lafuente-Arroyo S., Fernández-Caballero A. , López-Ferreras F. : An optimization on pictogram identification for the road-sign recognition task using SVMs, Computer Vision and Image Understanding 114 (3) , pp.373-383, 2010.
• [7] Garcia-Garrido M., Sotelo M., Martin-Gorostiza E.: Fast traffic sign detection and recognition under changing lighting conditions, In: Sotelo M (ed) Proceedings of the IEEE ITSC, pp.811–816, 2006.
• [8] Vicen Bueno R., Gil-Pita R., Rosa-Zurera M., Utrilla-Manso M., Lopez-Ferreras F.: Multilayer perceptrons applied to traffic sign recognition tasks, In: Proceedings of the 8th international work-conference on artificial neural networks, IWANN, Vilanovai la Geltru, Barcelona, Spain, pp.865–872, 2005.
• [9] Hsu S.H., Huang C.L.: Road sign detection and recognition using matching pursuit method, Image and Vision Computing 19, pp.119–129, 2001.
• [10] Prietoa M.S., Allen A.R.: Using self-organising maps in the detection and recognition of road signs, Image and Vision Computing 27(6), pp.673–683, 2009.
• [11] Pazhoumand-dar H., Yaghoobi M.: A new approach in road sign recognition based on fast fractal coding, Neural Computing and Applications, 22 no. 3-4, pp.615-625, 2013.
• [12] Hsien J.C., Liou Y.S., Chen S.Y.: Road Sign Detection and Recognition Using Hidden Markov Model, Asian Journal of Health and Information Sciences, Vol. 1 No. 1, pp.85-100, 2006.
• [13] Eickeler S., Müller S., Rigoll G.: High Performance Face Recognition Using Pseudo 2-D Hidden Markov Models, European Control Conference, http://citeseer.ist.psu.edu, 1999.
• [14] Vitoantonio Bevilacqua V., Cariello L., Carro G., Daleno D., Mastronardi G.: A face recognition system based on Pseudo 2D HMM applied to neural network coefficients, Soft Computing, 12, 7 (February), pp.615-621, 2008.
• [15] Li J., Najmi A., Gray R.M.: Image classification by a two dimensional Hidden Markov model, IEEE Transactions on Signal Processing, 48, pp.517-533, 2000.
• [16] Joshi D., Li J., Wang J.Z.: A computationally Efficient Approach to the estimation of two- and three-dimensional hidden Markov models, IEEE Transactions on Image Processing, vol. 15, no 7, pp.1871-1886, 2006.
• [17] Yujian L.: An analytic solution for estimating two-dimensional hidden Markov models, Applied Mathematics and Computation, 185,pp. 810-822, 2007.
• [18] Rabiner L. R.: A tutorial on hidden Markov models and selected application in speech recognition, Proc. IEEE, 77, pp.257-285, 1989.
• [19] Kanungo T.: Hidden Markov Model Tutorial, [web page] http://www.kanungo.com/software/hmmtut.pdf. [Accessed on 1 Jun. 2014.].
• [20] Bobulski J.: Hidden Markov Models for Two-dimensional data, Advances in Intelligent Systems and Computing, 226, Springer, pp.141-149, 2013.
• [21] Forney G.D.: The Viterbi Algorithm, Proc. IEEE, Vol. 61 No. 3, pp.268-278, 1973.
• [22] Samaria F., Young S.: HMM-based Architecture for Face Identification, Image and Vision Computing, Vol. 12 No 8 October, pp.537-583, 1994.
• [23] Stallkamp J., Schlipsing M., Salmen J., Igel C.: The German Traffic Sign Recognition Benchmark: A multi-class classification competition, In Proceedings of the IEEE International Joint Conference on Neural Networks, pp.1453–1460, 2011.
• [24] Database German Traffic Sign Benchmark, [web page] http://benchmark.ini.rub.de/Dataset/GTSRB-Final-Training-Images.zip. [Accessed on 1 Jun. 2014.].
• [25] Nguwi Y.Y., Cho S.Y.: Emergent self-organizing feature map for recognizing road sign images, Neural Computing and Application, 19, pp.601–615, 2010.