Skip to main content
Log in

Connection skeleton extraction based on contour connectedness

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

A stable skeleton is very important to some applications such as vehicle navigation, object represent and pattern recognition. The connection skeleton is just one that not only can be computed stably but also can figure the connectivity structure of contour. A new method named continuous connectivity detection and a new model named approximate regular polygon (ARP) were proposed for connection skeleton extraction. Both the method and the model were tested by the real maps of road network including flyovers, interchanges and other common object contours. Satisfactory results were obtained.

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.

Similar content being viewed by others

References

  1. Pavlidis T. A review of algorithms for shape analysis [J]. Computer Graphics and Image Processing, 1978, 7: 243–258.

    Article  Google Scholar 

  2. Blum H. A transformation for extracting new descriptors[C]//Symp Models for Perception of Speech and Visual Form. Cambridge: MIT Press, 1964.

    Google Scholar 

  3. Montanari U. Continuous skeletons from digitized images [J]. J Assoc, Comp Machinery, 1969, 16(4): 534–549.

    MATH  MathSciNet  Google Scholar 

  4. Bitz G, Kung H T. Path planning on the warp computer: Using a linear systolic array in dynamic programming [J]. Int’l J Computer Mathematics, 1988, 25: 173–188.

    Article  MATH  Google Scholar 

  5. Kimmel R. Skeletonization via distance maps and level sets [J]. Computer Vision and Image Understanding, 1995, 62(3): 382–391.

    Article  MathSciNet  Google Scholar 

  6. Leymarie F, Levine M D. Simulating the grassfire transform using an active contour model [J]. In IEEE Trans PAMI, 1992, 14(1): 56–75.

    Google Scholar 

  7. Brandt J W, Algazi V R. Continuous skeleton computation by voronoi diagram [J]. CVGIP: Image Understanding, 1994, 55: 329–338.

    Article  Google Scholar 

  8. Ogniewicz R L. Discrete voronoi skeletons [M]. Konstanz, Germany: Hartung-Gorre Verlag, 1993.

    Google Scholar 

  9. Liu T L, Geiger D, Kohn R V. Representation and self-similarity of shapes[C]//Proceedings of the Sixth International Conference on Computer Vision. Washington: IEEE Computer Society, 1999: 1129–1135.

    Google Scholar 

  10. Golland P, Eric W, Grimson L. Fixed topology skeleton[C]//Proceedings of the IEEE Computer Society Conference on Computer Visionand Pattern Recognftion. [s.l.]: IEEE, 2000: 10–17.

  11. Ejiri M, Kakumoto S, Miyatake T, et al. Automatic recognition of design drawings and maps[C]//in Proc Seventh Int. Conf Pattern Recognition. Montreal: [s.n.], 1984: 1296–1305.

  12. Kasturi R, Bow S T, El-Masri W, et al. A system for interpretation of line drawings [J]. IEEE Transactions on PAMI, 1990, 12(10): 978–992.

    Google Scholar 

  13. Suzuki S, Yamada T. MARIS: Map recognition input system [J]. Pattern Recognition, 1990, 23(8): 919–933.

    Article  Google Scholar 

  14. Nagao T, Agui T, Nakajima M. An automatic road vector extraction method from maps [C]//inProc Seventh Int. Conf Pattern Recognition. Montreal: [s.n.], 1988: 585–587.

  15. Liu Yuncai. An automation system: Generation of digital map data from pictorial resources [J]. Pattern Recognition, 2002, 35(9): 1973–1987.

    Article  MATH  Google Scholar 

  16. Bin D, Cheong W. A system for automatic extraction of road network from maps [C]//IEEE Proceedings, Intelligence and Systems. Washington DC: IEEE Computer Society, 1998: 359–366.

    Google Scholar 

  17. Itonaga W, Matsuda I, Yoneyama N, et al. Automatic extraction of road networks from map image [J]. Electronics and Communications in Japan, Part 2, 2003, 86(4): 62–72.

    Article  Google Scholar 

  18. Wolter F E. Cut locus and medial axis in global shape interrogation and representation [R]. MIT Design Laboratory Memorandum 92-2, Massachusettes: MIT Ocean Engineering Design Laboratory, 1992.

    Google Scholar 

  19. Chang J S, Yap C K. A polynomial solution for the potato-peeling problem [J]. Discrete & Computational Geometry, 1986, 1(1): 155–182.

    Article  MATH  MathSciNet  Google Scholar 

  20. Chen M, Cheng Z G, Liu Y C. A robust algorithm of principal curve detection [C]//ICPR. Washington DC: IEEE Computer Society, 2004: 429–432.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yun-cai Liu  (刘允才).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, M., Liu, Yc. Connection skeleton extraction based on contour connectedness. J. Shanghai Jiaotong Univ. (Sci.) 13, 521–527 (2008). https://doi.org/10.1007/s12204-008-0521-x

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-008-0521-x

Key words

CLC number

Navigation