Edge Point Linking by Means of Global and Local Schemes

  • Angel D. Sappa
  • Boris X. Vintimilla
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 31)


This chapter presents an efficient technique for linking edge points in order to generate a closed contour representation. The original intensity image, as well as its corresponding edge map, are assumed to be given as input to the algorithm (i.e., an edge map is previously computed by some of the classical edge detector algorithms). The proposed technique consists of two stages. The first stage computes an initial representation by connecting edge points according to a global measure. It relies on the use of graph theory. Spurious edge points are removed by a morphological filter. The second stage finally generates closed contours, linking unconnected edges, by using a local cost function. Experimental results with different intensity images are presented.3


Intensity Image Minimum Span Tree Triangular Mesh Edge Point Local Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    D. Ballard and C. Brown. Computer Vision. Prentice-Hall, Inc., 1982.Google Scholar
  2. 2.
    J. Canny. Computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6):679–698, 1986.CrossRefGoogle Scholar
  3. 3.
    A. Farag and E. Delp. Edge linking by sequential search. Pattern Recognition, 28(5):611–633, May 1995.CrossRefGoogle Scholar
  4. 4.
    M.A. Garcia, B. Vintimilla, and A. Sappa. Approximation and processing of intensity images with discontinuity-preserving adaptive triangular meshes. In Proc. ECCV 2000, D. Vernon, Ed. New York: Springer, 2000, vol. 1842, LNCS, Dublin, Ireland, pages 844–855, June/July 2000.Google Scholar
  5. 5.
    O. Ghita and P. Whelan. Computational approach for edge linking. Journal of Electronic Imaging, 11(4):479–485, October 2002.CrossRefGoogle Scholar
  6. 6.
    W.E. Grimson. From Images to Surfaces. Cambridge, MA: MIT Press, 1981.Google Scholar
  7. 7.
    A. Hajjar and T. Chen. A VLSI architecture for real-time edge linking. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(1):89–94, January 1999.Google Scholar
  8. 8.
    L. Hermes and J. Buhmann. A minimum entropy approach to adaptive image polygonization. IEEE Trans. on Image Processing, 12(10):1243–1258, October 2003.Google Scholar
  9. 9.
    C. Kim and J. Hwang. Fast and automatic video object segmentation and tracking for content-based applications. IEEE Trans. on Circuits and Systems for Video Technology, 12(2):122–1129, February 2002.Google Scholar
  10. 10.
    E. Saber and A. Tekalp. Integration of color, edge, shape, and texture features for automatic region-based image annotation and retrieval. Journal of Electronic Imaging, 7(3):684–700, July 1998.Google Scholar
  11. 11.
    A.D. Sappa. Unsupervised contour closure algorithm for range image edge-based segmentation. IEEE Trans. on Image Processing, 15(2):377–384, February 2006.Google Scholar
  12. 12.
    W. Snyder, R. Groshong, M. Hsiao, K. Boone, and T. Hudacko. Closing gaps in edges and surfaces. Image and Vision Computing, 10(8):523–531, October 1992.CrossRefGoogle Scholar
  13. 13.
    Y. Yang, M. Wernick, and J. Brankov. A fast approach for accurate contentadaptive mesh generation. IEEE Trans. on Image Processing, 12(8):866–881, August 2003.CrossRefMathSciNetGoogle Scholar
  14. 14.
    T. Zhang and C. Suen. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3):236–239, March 1984.Google Scholar
  15. 15.
    S. Zhu and A. Yuille. Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(9):884–900, 1996.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Angel D. Sappa
    • 1
  • Boris X. Vintimilla
    • 2
  1. 1.Computer Vision CenterEdifici O Campus UABBarcelonaSpain
  2. 2.Dept. of Electrical and Computer Science Engineering Escuela Superior Politecnica del LitoralVision and Robotics CenterEcuador

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