Skip to main content
Log in

Abstract

This paper presents a new algorithm to extract the skeleton and its Euclidean distance values from a binary image. The extracted skeleton reconstructs the objects in the image exactly. The algorithm runs in O(n) time for an image of size n × n. It involves simple local neighborhood operations for each pixel and hence it is quite amenable to VLSI implementation in a cellular architecture. Results of simulation of the algorithm in a sequential computer are presented. Results of implementation of a VLSI design in Xilinx FPGA are also presented and they confirm the speed and suitability of our method for real-time applications.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Y. Ge and J. Fitzpatrick, “On the Generation of Skeletons fromDiscrete Euclidean Distance Maps,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, 1996, pp. 1055–1066.

    Article  Google Scholar 

  2. C. Arcelli and G.S. di Baja, “A Width-Independent Fast Thinning Algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, 1985, pp. 463–47

    Article  Google Scholar 

  3. C. Arcelli and G.S. di Baja, “Euclidean Skeleton via Centreof-Maximal-Disc Extraction,” Image and Vision Computing, vol. 11, 1993, pp. 163–173.

    Article  Google Scholar 

  4. F. Leymarie and M. Levine, “Simulating the Grassfire Transform using an Active Contour Model,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, 1992, pp. 56–75.

    Article  Google Scholar 

  5. E. Liang and E. Wong, “An Efficient Method for Obtaining Morphological Skeleton,” Pattern Recognition Letters, vol. 14, 1993, pp. 689–695.

    Article  Google Scholar 

  6. F. Shih and C. Pu, “A Skeletonization Algorithm by Maxima Tracking on Euclidean Distance Transform,” Pattern Recognition, vol. 28, no. 3, 1995, pp. 331–341.

    Article  Google Scholar 

  7. C. Niblack, P. Gibbons, and D. Capson, “Generating Skeletons and Centerlines from the Distance Transform,” CVGIP: Graphical Models and Image Processing, vol. 54, 1992, pp. 420–437.

    Google Scholar 

  8. B. Jang and R. Chin, “Analysis of Thinning Algorithms using Mathematical Morphology,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, 1990, pp. 541–551.

    Article  Google Scholar 

  9. C. Arcelli and G.S. di Baja, “A One-Pass Two Operation Process to Detect the Skeletal Pixels on the 4-Distance Transform,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 4, 1989, pp. 411–414.

    Article  Google Scholar 

  10. G.S. di Baja, “Well-Shaped, Stable and Reversible Skeletons from the (3,4)-Distance Transform,” Journal of Visual Communication and Image Representation, vol. 5, 1994, pp. 107–115.

    Article  Google Scholar 

  11. G.S. di Baja and E. Thiel, “(3,4)-Weighted Skeleton Decomposition for Pattern Representation and Description,” Pattern Recognition, vol. 27, no. 8, 1994, pp. 1037–1049.

    Google Scholar 

  12. Y. Chehadeh, D. Coquin, and P. Bolon, “A Skeletonization Algorithm Using Chamfer Distance Transformation Adapted to Rectangular Grids,” in Proceedings of IEEE International Conference on Pattern Recognition, 1996.

  13. G.S. di Baja and E. Thiel, “Skeletonization Algorithm Running on Path-Based Distance Maps,” Image and Vision Computing, vol. 14, 1996, pp. 47–57.

    Article  Google Scholar 

  14. C. Arcelli and G.S. di Baja, “Skeletons of Planar Patterns,” in Topological Algorithms for Digital Image Processing, T. Kong and A. Rosenfeld (Eds.), Elsevier, 1996, pp. 99–143.

  15. N. Ranganathan and K. Doreswamy, “A VLSI Chip for Computing the Medial Axis Transform of an Image,” in Proceedings of IEEE International Conference on Computer Architecture and Machine Perception, 1995.

  16. F. Klein and O. Kubler, “Euclidean Distance Transformations and Model-Guided Image Interpretation,” Pattern Recognition, vol. 5, 1987, pp. 19–29.

    Article  Google Scholar 

  17. J. Brandt and V. Algazi, “Computing a Stable, Connected Skeleton from Discrete Data,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991.

  18. C. Arcelli, L. Cordella, and S. Levialdi, “From Local Maxima to Connected Skeleton,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, 1981, pp. 134–143.

  19. L. Dorst, “Pseudo-Euclidean Skeletons,” in Proceedings of IEEE International Conference on Pattern Recognition, 1986, pp. 286–288.

  20. C. Arcelli and G.S. di Baja, “Finding Local Maxima in a Pseudo-Euclidean Distance Transform,” Computer Vision, Graphics and Image Processing, vol. 43, 1988, pp. 361–367.

    Article  Google Scholar 

  21. F. Shih and C. Pu, “Medial Axis Transformation with Single-Pixel and Connectivity Preservation Using Euclidean Distance Computation,” in Proceedings of IEEE International Conference on Pattern Recognition, 1990.

  22. F. Shih and C. Pu, “AMaxima-Tracking Method for Skeletonization from Euclidean Distance Function,” in Proceedings of International Conference on Tools for AI, 1991.

  23. C. Arcelli and G.S. di Baja, “Ridge Points in Euclidean Distance Maps,” Pattern Recognition Letters, vol. 13, 1992, pp. 237–243.

    Article  Google Scholar 

  24. IEEE Standard VHDL Language Reference Manual. The Institute of Electrical and Electronics Engineers, 1993.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sudha, N. Design of a Cellular Architecture for Fast Computation of the Skeleton. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 35, 61–73 (2003). https://doi.org/10.1023/A:1023335904573

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1023335904573

Navigation