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An Effective Graph-Cut Segmentation Approach for License Plate Detection

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Recent Trends in Image and Signal Processing in Computer Vision

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1124))

Abstract

Despite the successes of license plate detection (LPD) methods in the past decades, only a few methods can effectively detect multi-style license plates (LPs), especially those from different countries. This paper addresses the challenge of LPD by using an automatic graph-cut-based segmentation approach to effectively detect LPs of varying sizes, colors, backgrounds, distances, and orientations. To evaluate our proposed approach, a developed algorithm was tested on 1050 vehicle images. An accuracy and average processing time of 98.67% and 0.1 s were achieved for the detection of LPs, respectively. Experimental results show that the proposed method can detect LPs from both the front and back view of vehicles and also vehicles with skew orientation. Toward the end, a comparison of results with existing methods is also reported.

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References

  1. K.T. Thomas, J. Vaijayanthi, A review of automatic license plate detection using edge detection methods. Int. J. Res. Appl. Sci. Eng. Technol. 2(V), 18–22 (2014)

    Google Scholar 

  2. A.M. Al-Ghaili, S. Mashohor, A. Ramli, A. Ismail, Vertical-edge-based car license-plate detection method. IEEE Trans. Veh. Technol. 62(1), 26–38 (2013)

    Article  Google Scholar 

  3. A. Roy, D.P. Ghoshal, Number plate recognition for use in different countries using an improved segmentation. IEEE 1–3 (2011)

    Google Scholar 

  4. A.O. Salau, Development of a vehicle plate number localization technique using computer vision. Ph.D. Dissertation. Obafemi Awolowo University, Ile-Ife, Nigeria, p. 200 (2018)

    Google Scholar 

  5. A.O. Salau, T.K. Yesufu, B.S. Ogundare, Vehicle plate number localization using a modified grabcut algorithm. J. King Saud Univ. Comput. Inf. Sci. (2019). https://doi.org/10.1016/j.jksuci.2019.01.011

  6. Z. Fu, L. Wang, Color image segmentation using gaussian mixture model and em algorithm. Springer 346, 61–66 (2012)

    Google Scholar 

  7. F. Yi, I. Moon, Image segmentation: a survey of graph-cut methods, in IEEE International Conference on Systems and Informatics (ICSAI) (2012), pp. 1936–1941

    Google Scholar 

  8. B. Basavaprasad, R.S. Hegadi, Improved grabcut technique for segmentation of color image. Int. J. Comput. Appl. (2014) 5–8

    Google Scholar 

  9. W.S. Chowdhury, A.R. Khan, J. Uddin, Vehicle license plate detection using image segmentation and morphological image processing, in International Symposium on Signal Processing and Intelligent Recognition Systems (2018), pp. 142–154. https://doi.org/10.1007/978-3-319-67934-1_13

    Google Scholar 

  10. V. Franc, V. Hlaváč, License plate character segmentation using hidden markov chains, in Joint Pattern Recognition Symposium (2005), pp. 385–392. https://doi.org/10.1007/11550518_48

    Google Scholar 

  11. D. Khattab, H.M. Ebeid, F.M. Tolba, A.S. Hussein, Clustering-based image segmentation using automatic grabcut, in Proceedings of the 10th International Conference on Informatics and Systems (2016), pp. 95–100

    Google Scholar 

  12. C. Hung, Y. Chen, Y. Chang, S. Ruan, An efficient thresholding algorithm for license plate recognition based on intelligent block detection, in 4th IEEE Conference on Industrial Electronics and Applications, Xi’an (2009), pp. 236–240. https://doi.org/10.1109/iciea.2009.5138203

  13. T. Panchal, H. Patel, A. Panchal, License plate detection using harris corner and character segmentation by integrated approach from an image. Proc. Comput. Sci. 79, 419–425 (2016). https://doi.org/10.1016/j.procs.2016.03.054

    Article  Google Scholar 

  14. H. Hendry, C. Chen, Automatic license plate recognition via sliding-window darknet-YOLO deep learning. Image Vis. Comput. 87, 47–56 (2019). https://doi.org/10.1016/j.imavis.2019.04.007

    Article  Google Scholar 

  15. A.C. Roy, M.K. Hossen, D. Nag, License plate detection and character recognition system for commercial vehicles based on morphological approach and template matching, in 3rd IEEE International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka. (2016), pp. 1–6. https://doi.org/10.1109/ceeict.2016.7873098

  16. H.V. Dastjerdi, V. Rostami, F. Kheiri, Automatic license plate detection system based on the point weighting and template matching, in 7th IEEE Conference on Information and Knowledge Technology (IKT), Iran (2015), pp. 1–5. https://doi.org/10.1109/ikt.2015.7288783

  17. C. Hsieh, Y. Juan, K. Hung, Multiple license plate detection for complex background, in 19th IEEE International Conference on Advanced Information Networking and Applications (AINA’05), Taipei, Taiwan (2005), pp. 389–392. https://doi.org/10.1109/aina.2005.257

  18. E.N. Mortensen, W.A. Barrett, Intelligent scissors for image composition, in Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (1995), pp. 191–198

    Google Scholar 

  19. Y. Boykov, M.P. Jolly, Interactive graph-cuts for optimal boundary and region segmentation of objects in N-D images, in International Conference on Computer Vision. I (2001), pp. 105–112

    Google Scholar 

  20. C. Rother, V. Kolmogorov, A. Blake, GrabCut interactive foreground extraction using iterated graph-cuts. ACM Trans. Graph. 309–314 (2004)

    Google Scholar 

  21. D. Freedman, T. Zhang, Interactive graph cut based segmentation with shape priors, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (2005), p. 8

    Google Scholar 

  22. F. Wang, S. Ferraro, L. Lin, T.S. Mayor, V. Molinaro, M. Ribeiro, Localised boundary air layer and clothing evaporative resistances for individual body segments. J. Egronomics 55(7), 799–812 (2012). Taylor and Francis

    Google Scholar 

  23. Z. Yu, M. Xu, Z. Gao, Biomedical image segmentation via constrained graph-cuts and pre-segmentation, in IEEE International Conference on EMBC (2011), pp. 5714–5717

    Google Scholar 

  24. Online CV Database: http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.html, www.cvpapers.com/datasets.html, www.medialab.ntua.gr/research/LPRdatabase.html

  25. A.O. Salau, S. Jain, Feature extraction: a survey of the types, techniques and applications, in 5th IEEE International Conference on Signal Processing and Communication (ICSC-2019), Noida, India

    Google Scholar 

  26. Y. Boykov, O. Veksler, Graphcuts in vision and graphics: theories and applications, in Handbook of mathematical models in computer vision (2005), pp. 100–118

    Google Scholar 

  27. S. Yang, J. Jiang, M. Wu, C.C. Ho, Real-time license plate detection system with 2-level 2D Haar Wavelet transform and Wiener-deconvolution vertical edge enhancement, in 9th IEEE International Conference on Information, Communications and Signal Processing, Tainan (2013), pp. 1–5. https://doi.org/10.1109/icics.2013.6782805

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Authors and Affiliations

  1. Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria

    Ayodeji Olalekan Salau

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  1. Ayodeji Olalekan Salau
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Correspondence to Ayodeji Olalekan Salau .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Shruti Jain

  2. Department of Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India

    Sudip Paul

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Salau, A.O. (2020). An Effective Graph-Cut Segmentation Approach for License Plate Detection. In: Jain, S., Paul, S. (eds) Recent Trends in Image and Signal Processing in Computer Vision. Advances in Intelligent Systems and Computing, vol 1124. Springer, Singapore. https://doi.org/10.1007/978-981-15-2740-1_2

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