Skip to main content
SpringerLink
Log in

Selecting Anchor Points for 2D Skeletonization

  • Conference paper
Image Analysis and Recognition (ICIAR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6753))

Included in the following conference series:

Abstract

In this paper two criteria are presented to compute reduced sets of centers of maximal discs in the weighted <3,4> distance transform of 2D digital patterns. The centers of maximal discs selected by the above criteria are used as anchor points in the framework of 2D skeletonization and, depending on the adopted criterion, originate skeletons with different properties.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lam, L., Lee, S.W., Suen, C.Y.: Thinning methodologies-a comprehensive survey. IEEE Trans. PAMI 14(9), 869–885 (1992)

    Article  Google Scholar 

  2. Saeed, K., Tabedzki, M., Rybnik, M., Adamski, M.: K3M: a universal algorithm for image skeletonization and a review of thinning techniques. Int. J. Appl. Math. Comput. Sci. 20(2), 317–335 (2010)

    Article  MATH  Google Scholar 

  3. Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the Perception of Speech and Visual Form, pp. 362–380. MIT Press, Cambridge (1967)

    Google Scholar 

  4. Arcelli, C., Sanniti di Baja, G.: A width-independent fast thinning algorithm. IEEE Trans. PAMI 7, 463–474 (1985)

    Article  Google Scholar 

  5. Klein, F.: Euclidean skeletons. In: Proc. 5th Scand. Conf. Image Anal., pp. 443–450 (1987)

    Google Scholar 

  6. Arcelli, C., Sanniti di Baja, G.: A one-pass two-operations process to detect the skeletal pixels on the 4-distance transform. IEEE Trans. PAMI 11, 411–414 (1989)

    Article  Google Scholar 

  7. Xia, Y.: Skeletonization via the realization of the fire front’s propagation and extinction in digital binary shapes. IEEE Trans. PAMI 11(10), 1076–1086 (1989)

    Article  Google Scholar 

  8. Arcelli, C., Sanniti di Baja, G.: Euclidean skeleton via center-of-maximal-disc extraction. Image and Vision Computing 11, 163–173 (1993)

    Article  Google Scholar 

  9. Kimmel, R., Shaked, D., Kiryati, N.: Skeletonization via distance maps and level sets. Computer Vision and Image Understanding 62(3), 382–391 (1995)

    Article  Google Scholar 

  10. Sanniti di Baja, G., Thiel, E.: Skeletonization algorithm running on path-based distance maps. Image and Vision Computing 14, 47–57 (1996)

    Article  Google Scholar 

  11. Svensson, S., Borgefors, G., Nystrom, I.: On reversible skeletonization using anchor-points from distance transforms. Journal of Visual Communication and Image Representation 10, 379–397 (1999)

    Article  Google Scholar 

  12. Mekada, Y., Toriwaki, J.I.: Anchor point thinning using a skeleton based on the Euclidean distance transformation. In: Proceedings of ICPR 2002, vol. 3, pp. 923–926 (2002)

    Google Scholar 

  13. Serino, L., Arcelli, C., Sanniti di Baja, G.: A Characterization of Centers of Maximal Balls in the <3,4,5> weighted distance transform of 3D digital objects. In: Proceedings WADGMM, pp. 12–16 (2010)

    Google Scholar 

  14. Arcelli, C., Sanniti di Baja, G., Serino, L.: Distance driven skeletonization in voxel images. IEEE Trans. PAMI (2010), http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.140

  15. Yamashita, M., Ibaraki, T.: Distances defined by neighborhood sequences. Pattern Recognition 19(3), 237–246 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  16. Das, P.P., Chakrabarti, P.P., Chatterji, B.N.: Distance functions in digital geometry. Information Sciences 42, 113–136 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  17. Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics, and Image Processing 34(3), 344–371 (1986)

    Article  Google Scholar 

  18. Yokoi, S., Toriwaki, J.I., Fukumura, T.: An analysis of topological properties of digitized binary pictures using local features. Comp. Graphics and Image Processing 4, 63–73 (1975)

    Article  MathSciNet  Google Scholar 

  19. Arcelli, C., Sanniti di Baja, G.: Weighted distance transforms: a characterization. In: Cantoni, V., Di Gesù, V., Levialdi, S. (eds.) Image Analysis and Processing II, pp. 205–211. Plenum Press, New York (1988)

    Chapter  Google Scholar 

  20. http://www.lems.brown.edu/~dmc/

Download references

Author information

Authors and Affiliations

  1. Institute of Cybernetics “E. Caianiello”, CNR, Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy

    Luca Serino & Gabriella Sanniti di Baja

Authors
  1. Luca Serino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriella Sanniti di Baja
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Serino, L., di Baja, G.S. (2011). Selecting Anchor Points for 2D Skeletonization. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21593-3_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21592-6

  • Online ISBN: 978-3-642-21593-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation