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License plate localization based on a probabilistic model

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Abstract

Extraction of the license plate region is the challenging first step in the license plate recognition system. We propose a novel feature fusion concept for plate extraction. The image-feature extraction process is modeled as a feature-detection problem in noise. The geometric features are probabilistically modeled and detected under various detection thresholds. These detection results are then fused within the Bayesian framework to obtain the features for further processing. Along with a probabilistic model, a pixels voting algorithm is also tested through threshold variation.

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References

  1. JXifan S., Weizhong Z., Yonghang S.: Automatic license plate recognition system based on color image processing. ICCSA 4, 1159–1168 (2005)

    Google Scholar 

  2. Tsang-Hong, W., Feng-Chou, N., Keh-Tsong, L., Yon-Ping, C.: Robust license plate recognition based on dynamic projection warping. In: Proceeding of IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 784–788 (2004)

  3. Dai, Y., Ma, H., Liu, J., Li, L.: A high performance license plate recognition system based on the web technique. In: Proceedings of IEEE Intelligent Transportation Systems, pp. 325–329 (2001)

  4. David, L., Andres, M., Vicente, P., Juan, V.: Car license plates extraction and recognition based on connected components analysis and HMM decoding. In: IbPRIA, vol. 1, pp. 571–578 (2005)

  5. Comelli, P., Ferragina, P., Notturno, M.: Optical recognition of motorvehicle license plates. In: Vehicular Technology, vol. 44, No. 4, 790–799 (1995)

  6. Otsu, N.: A threshold selection method from gray level histograms. In: IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-9, pp. 62–66 (1979)

  7. Bai, H., Liu, C.: A hybrid license plate extraction method based on edge statistics and morphology. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 831–834. 23–26 Aug (2004)

  8. Parker, J., Federl, P.: An approach to license plate recognition. In: Computer Science Technical reports, University of Calgary, Alberta Canada, 591–11, October (1996)

  9. Tran, D., Tran D., Tran, P., Nguyen, H.: Building an automatic vehicle license-plate recognition system. In: 3rd International Conference on Computer Science—Recherche, Innovation & Vision du Futur (RIVF2005), Can Tho, Vietnam, 21–24 Feb (2005)

  10. Beatriz, A.: Experiments in image segmentation for automatic US license plate recognition. Dissertation, Virginia Polytechnic Institute and State University (2004)

  11. Anagnostopoulos E., Anagnostopoulos E., Loumos V., Kayafas E.: A license plate-recognition algorithm for intelligent transportation system applications. IEEE Trans. Intell. Transp. Syst. 7(3), 377–392 (2006)

    Article  Google Scholar 

  12. Dlagnekov, L.: Video-based car surveillance: License plate, make, and model recognition. Dissertation, U.C. San Diego (2005)

  13. Viola, P., Jones, M.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: NIPS, vol. 14 (2002)

  14. Matas, J., Zimmermann, K.: Unconstrained licence plate Detection. In: Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, pp. 572–577, isbn 07803-9216-7, Wien, Austria (2005)

  15. Elliman, G., Lanxaster, T.: A review of segmentation and contextual analysis techniques for text recognition. In: Pattern Recognition, vol. 23, No. 3, 337–346 (1990)

  16. Broumandnia, A., Fathy, M.: Application of pattern recognition for Farsi license plate recognition. In: Vision and Image Processing (GVIP), Dec (2005)

  17. Barroso, J., Bulas-Cruz, J., Dagless, L.: Real time number plate reading. In: 4th IFAC Workshop on Algorithms and Architectures for Real-time Control, Portugal, April (1997)

  18. Ching-Tang, H., Yu-Shan, Kuo-Ming: Multiple license plate detection for complex background. In: Proceedings of 19th International Conference on Advanced Information Networking and Applications, vol. 2, pp. 389–392 (2005)

  19. Nijhuis, J., Brugge, M., Helmholt, K., Pluim, J., Spaanenburg, L., Venema, R., Westenberg, M.: Car license plate recognition with neural networks and fuzzy logic. In: Proceedings of IEEE International Conference on Neural Networks, vol. 5, pp. 2232–2236 (1995)

  20. Zimic, N., Ficzko, J., Mraz, M., Virant, J.: The fuzzy logic approach to the car number plate locating problem. In: Proceedings of Intelligent Information Systems, IIS ’97, 227–230 (1997)

  21. Shyang-Lih, C., Li-Shien, C., Yun-Chung, C., Sei-Wan, C.: Automatic license plate recognition. In: IEEE Transactions on Intelligent Transportation Systems, vol. 5, No. 1, pp. 42–53 (2004)

  22. Park, H., Kim, I., Jung, K., Kim, J.: Locating car license plates using neural networks. In: Electronics Letters, vol. 35, No. 17, pp. 1475–1477 (1999)

  23. Chacon, I., Zimmerman, A.: License plate location based on a dynamic PCNN scheme. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1195–1200. 20-24 July (2003)

  24. Rahman R., Fairhurst C.: Multiple classifier decision combination strategies for character recognition: a review. Int. J. Doc. Anal. Recognit. 5(4), 166–194 (2003)

    Article  Google Scholar 

  25. Huang, Z., Guo, Y.: Classifier fusion based vehicle license plate detection algorithm. In: proceedings of the Second International Conference on Machine Learning and Cybernetics, pp. 2984–2989 (2003)

  26. Finlayson, G., Hordley, S., Hubel, P.: Color by correlation: A simple,unifying framework for color constancy. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, No. 11, pp. 1209–1221 (2001)

  27. Rajaram, S., Gupta, S., Petrovic, N., Huang, S.: Learning-based nonparametric image super-resolution. In: EURASIP Journal on Applied Signal Processing, vol. 2006, Article ID 51306, p. 111 doi:10.1155/ASP/2006/5130, (2006)

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Authors and Affiliations

  1. Faculty of Engineering, University of Technology, Sydney, Broadway, PO Box 123, Sydney, NSW, 2007, Australia

    Rami Al-Hmouz

  2. Faculty of Engineering, The University of Melbourne, Melbourne, Australia

    Subhash Challa

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  1. Rami Al-Hmouz
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  2. Subhash Challa
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Correspondence to Rami Al-Hmouz.

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Al-Hmouz, R., Challa, S. License plate localization based on a probabilistic model. Machine Vision and Applications 21, 319–330 (2010). https://doi.org/10.1007/s00138-008-0164-9

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  • DOI: https://doi.org/10.1007/s00138-008-0164-9

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