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A modified ZS thinning algorithm by a hybrid approach

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Abstract

Thinning is one of the most important techniques in the field of image processing. It is applied to erode the image of an object layer-by-layer until a skeleton is left. Several thinning algorithms allowing to get a skeleton of a binary image are already proposed in the literature. This paper investigates several well-known parallel thinning algorithms and proposes a modified version of the most widely used thinning algorithm, called the ZS algorithm. The proposed modified ZS (MZS) algorithm is implemented and compared against seven existing algorithms. Experimental results and performances evaluation, using different image databases, confirm the proposed MZS algorithm improvement over the seven examined algorithms both in terms of the obtained results quality and the computational speed. Moreover, for an efficient implementation (on Graphics Processing Units), a parallel model of the MZS algorithm is proposed (using the Compute Unified Device Architecture, CUDA, as a parallel programming model). Evaluation results have shown that the parallel version of the proposed algorithm is, on average, more than 21 times faster than the traditional CPU sequential version.

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References

  1. Ahmed, M., Ward, R.: A rotation invariant rule-based thinning algorithm for character recognition. IEEE Trans. Pattern Anal Mach Intell 24(12), 1672–1678 (2002)

    Article  Google Scholar 

  2. Boudaoud, L.B., Sider, A., Tari, A.: A new thinning algorithm for binary images. In: Control, Engineering and Information Technology (CEIT), 2015 3rd International Conference on, pp. 1–6. IEEE (2015)

  3. Chen, W., Sui, L., Xu, Z., Lang, Y.: Improved Zhang-Suen thinning algorithm in binary line drawing applications. In: Systems and Informatics (ICSAI), 2012 International Conference on, pp. 1947–1950. IEEE (2012)

  4. Cheng, J., Grossman, M., McKercher, T.: Professional Cuda C Programming. John Wiley & Sons, London (2014)

    Google Scholar 

  5. Couturier, R.: Designing Scientific Applications on GPUs. Chapman and Hall/CRC, Boca Raton (2013)

    MATH  Google Scholar 

  6. Deng, W., Iyengar, S.S., Brener, N.E.: A fast parallel thinning algorithm for the binary image skeletonization. Int. J. High Perform. Comput. Appli. 14(1), 65–81 (2000)

    Article  Google Scholar 

  7. Jagna, A., Kamakshiprasad, V.: New parallel binary image thinning algorithm. ARPN J. Eng. Appl. Sci. 5(4), 64–67 (2010)

    Google Scholar 

  8. Kocharyan, D.: An efficient fingerprint image thinning algorithm. Am. J. Softw. Eng. Appl. 2(1), 1–6 (2013)

    MathSciNet  Google Scholar 

  9. Kong, T.Y., Rosenfeld, A.: Topological Algorithms for Digital Image Processing, vol. 19. Elsevier, Amsterdam (1996)

    Book  Google Scholar 

  10. Lam, L., Lee, S.W., Suen, C.Y.: Thinning methodologies-a comprehensive survey. IEEE Trans. Pattern Anal. Mach. intell. 14(9), 869–885 (1992)

    Article  Google Scholar 

  11. Lü, H., Wang, P.S.P.: A comment on a fast parallel algorithm for thinning digital patterns. Commun. ACM 29(3), 239–242 (1986)

    Article  Google Scholar 

  12. Palágyi, K.: A 3-subiteration 3D thinning algorithm for extracting medial surfaces. Pattern Recognit. Lett. 23(6), 663–675 (2002)

    Article  MATH  Google Scholar 

  13. Rosenfeld, A.: Digital Picture Processing. Academic Press, New York (1976)

    MATH  Google Scholar 

  14. Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley, Upper Saddle River (2010)

    Google Scholar 

  15. Song, C., Pang, Z., Jing, X., Xiao, C.: Distance field guided \(L_{1}\)-median skeleton extraction. Vis. Comput (2016). doi:10.1007/s00371-016-1331-z

  16. Tang, Y.Y., You, X.: Skeletonization of ribbon-like shapes based on a new wavelet function. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1118–1133 (2003)

    Article  Google Scholar 

  17. Tarabek, P.: A robust parallel thinning algorithm for pattern recognition. In: Applied Computational Intelligence and Informatics (SACI), 2012 7th IEEE International Symposium on, pp. 75–79. IEEE (2012)

  18. Wolberg, G.: Skeleton-based image warping. Vis. Comput. 5(1), 95–108 (1989)

    Article  Google Scholar 

  19. Wu, R.Y., Tsai, W.H.: A new one-pass parallel thinning algorithm for binary images. Pattern Recognit. Lett. 13(10), 715–723 (1992)

    Article  Google Scholar 

  20. Zhang, T., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)

    Article  Google Scholar 

  21. Zhou, R., Quek, C., Ng, G.S.: A novel single-pass thinning algorithm and an effective set of performance criteria. Pattern Recognit. Lett. 16(12), 1267–1275 (1995)

    Article  Google Scholar 

Download references

Acknowledgements

This work was carried out in the framework of research activities of the laboratory LIMED which is affiliated to the Faculty of Exact Sciences of the University of Bejaia and the Image and Information processing department of IMT Atlantique Institute. The authors would like to thank the referees for their comments.

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Authors and Affiliations

  1. Laboratoire d’Informatique Médicale, LIMED, Faculté des Sciences Exactes, Université de Bejaia, 06000, Bejaia, Algeria

    Lynda Ben Boudaoud & Abdelkamel Tari

  2. Image and Information Processing Department, IMT Atlantique Institute, 29238, Brest, France

    Basel Solaiman

Authors
  1. Lynda Ben Boudaoud
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  2. Basel Solaiman
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  3. Abdelkamel Tari
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Correspondence to Lynda Ben Boudaoud.

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Ben Boudaoud, L., Solaiman, B. & Tari, A. A modified ZS thinning algorithm by a hybrid approach. Vis Comput 34, 689–706 (2018). https://doi.org/10.1007/s00371-017-1407-4

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  • DOI: https://doi.org/10.1007/s00371-017-1407-4

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