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Skeletonization Based on K-Nearest-Neighbors on Binary Image

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MultiMedia Modeling (MMM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13142))

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Abstract

Skeletonization on binary image is a process for reducing foreground regions to a skeletal remnant, which largely preserves the original region’s connectivity. An algorithm for skeletonization based on k-nearest-neighbors is proposed in this paper. Instead of the fixed 8-neighborhood approach, which is the most common in the thinning field, our algorithm implements skeletonization based on k-nearest-neighbors. The method mainly consists of two stages: raw skeleton extraction and a novel thinning for post-processing. Extensive experiments are conducted and results show that the skeleton extracted by our method is precise, clean, and much smoother than the previous works.

Supported by the Chongqing Research Program of Basic Research and Frontier Technology (Grant No. cstc2019jcyj-msxmX0033), National Key R&D Program of China (Grant No. 2019YFD1100501) and National Natural Science Foundation of China (Grant No. 61701051).

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References

  1. Baja, G., Thiel, E.: Skeletonization algorithm running on path-based distance maps. Image Vis. Comput. 14(1), 47–57 (1996)

    Article  Google Scholar 

  2. Ben Boudaoud, L., Solaiman, B., Tari, A.: A modified ZS thinning algorithm by a hybrid approach. Vis. Comput. 34(5), 689–706 (2017). https://doi.org/10.1007/s00371-017-1407-4

    Article  Google Scholar 

  3. Bertrand, G., Couprie, M.: Powerful parallel and symmetric 3D thinning schemes based on critical kernels. J. Math. Imaging Vis. 48(1), 134–148 (2012). https://doi.org/10.1007/s10851-012-0402-7

    Article  MathSciNet  MATH  Google Scholar 

  4. Bilal, B.: An iterative thinning algorithm for binary images based on sequential and parallel approaches. Representation, Processing, Analysis, and Understanding of Image, pp. 34–43 (2018)

    Google Scholar 

  5. Blum, H.: A transformation for extracting new descriptors of shape. Models Percept. Speech Vis. 19, 362–380 (1967)

    Google Scholar 

  6. Boiman, O., Shechtman, E.: In defense of nearest-neighbor image classification (2008)

    Google Scholar 

  7. Borgefors, G., Baja, G.: Skeletonizing the distance transform on the hexagonal grid. In: 9th International Conference on Pattern Recognition, 1988 (1988)

    Google Scholar 

  8. Choi, W.P., Lam, K.M., Siu, W.C.: Extraction of the Euclidean skeleton based on a connectivity criterion. Pattern Recognit. 36(3), 721–729 (2003)

    Article  Google Scholar 

  9. Durix, B., Morin, G., Chambon, S., Mari, J.L., Leonard, K.: One-step compact skeletonization. In: Cignoni, P., Miguel, E. (eds.) Eurographics 2019 - Short Papers. The Eurographics Association (2019). https://doi.org/10.2312/egs.20191005

  10. Franti, P., Virmajoki, O., Hautamaki, V.: Fast agglomerative clustering using a k-nearest neighbor graph. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1875–1881 (2006)

    Article  Google Scholar 

  11. Latecki, L.J., Li, Q., Xiang, B., Liu, W.: Skeletonization using SSM of the distance transform. IEEE (2007)

    Google Scholar 

  12. Meijster, A., Roerdink, J.B.T.M., Hesselink, W.H.: A general algorithm for computing distance transforms in linear time. in: Goutsias, J., Vincent, L., Bloomberg, D.S. (eds.) Mathematical Morphology and its Applications to Image and Signal Processing. CIV, vol. 18, pp. 331–340. Springer, Boston (2002). https://doi.org/10.1007/0-306-47025-X_36

  13. Palágyi, K.: Equivalent 2D sequential and parallel thinning algorithms. In: International Workshop on Combinatorial Image Analysis (2014)

    Google Scholar 

  14. Piotrowska, M., Kostek, B., Ciszewski, T., Cyzewski, A.: Machine learning based analysis of English lateral allophones. Int. J. Appl. Math. Comput. Sci. 29(2), 393–405 (2019)

    Article  Google Scholar 

  15. Tabedzki, M., Saeed, K., Szczepański, A.: A modified K3M thinning algorithm. Int. J. Appl. Math. Comput. Sci. 26(2), 439–450 (2016)

    Article  MathSciNet  Google Scholar 

  16. Ye, Q.Z.: The signed Euclidean distance transform and its applications. In: 9th International Conference on Pattern Recognition, 1988 (1988)

    Google Scholar 

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

    Google Scholar 

  18. Zhou, J., Liu, J., Zhang, M.: Curve skeleton extraction via k-nearest-neighbors based contraction. Int. J. Appl. Math. Comput. Sci. 30, 123–132 (2020)

    MATH  Google Scholar 

  19. Zhou, R.W., Quek, C., Ng, G.S.: A novel single-pass thinning algorithm and an effective set of performance criteria. Pattern Recognt. Lett. 16(12), 1267–1275 (1995). https://doi.org/10.1016/0167-8655(95)00078-X

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Ren, Y., Zhang, M., Zhou, H., Liu, J. (2022). Skeletonization Based on K-Nearest-Neighbors on Binary Image. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_21

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  • DOI: https://doi.org/10.1007/978-3-030-98355-0_21

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  • Print ISBN: 978-3-030-98354-3

  • Online ISBN: 978-3-030-98355-0

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