Topology Preservation and Tricky Patterns in Gray-Tone Images

  • Carlo Arcelli
  • Luca Serino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2886)

Abstract

A gray-tone image including perceptually meaningful elongated regions can be represented by a set of line patterns, the skeleton, consisting of pixels having different gray-values and mostly placed along the central positions of the regions themselves. We discuss a skeletonization algorithm, computed over the Distance Transform of the image and employing topology preserving operations. Differently from the binary case, where the use of the connectivity test is generally sufficient to create a one-pixel-thick skeleton, we consider also a suitable labeling of the pixel neighborhood. In this way, we are able to deal with some of the tricky patterns in the gray-tone image that can be regarded as irreducible.

Keywords

Feature Point Binary Image Adjacent Region Simple Point Distance Transform 
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.

References

  1. 1.
    Wang, L., Pavlidis, T.: Detection of curved and straight segments from grey scale topography. CVGIP: Image Understanding 58, 352–365 (1993)CrossRefGoogle Scholar
  2. 2.
    Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. In: Dougherty, E.R. (ed.) Mathematical Morphology in Image Processing, pp. 433–481. Marcel Dekker, New York (1993)Google Scholar
  3. 3.
    Goetcherian, V.: From binary to grey tone image processing using fuzzy logic concepts. Pattern Recognition 12, 7–15 (1980)CrossRefGoogle Scholar
  4. 4.
    Bertrand, G., Everat, J.-C., Couprie, M.: Image segmentation through operators based on topology. J. Electronic Imaging 6, 395–405 (1997)CrossRefGoogle Scholar
  5. 5.
    Rosenfeld, A.: On connectivity properties of greyscale pictures. Pattern Recognition 16, 47–50 (1983)CrossRefGoogle Scholar
  6. 6.
    Piper, J., Granum, E.: Computing distance transformations in convex and non-convex domains. Pattern Recognition 20, 599–615 (1987)CrossRefGoogle Scholar
  7. 7.
    Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics and Image Processing 34, 344–371 (1986)CrossRefGoogle Scholar
  8. 8.
    Gilbert, E.N.: Lattice-theoretic properties of frontal switching functions. J. Math. Phys. 33, 57–67 (1954)MATHGoogle Scholar
  9. 9.
    Yokoi, S., Toriwaki, J.-I., Fukumura, T.: An analysis of topological properties of digitized binary pictures using local features. Computer Graphics and Picture Processing 4, 63–73 (1975)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Arcelli, C.: Topological changes in grey-tone digital pictures. Pattern Recognition 32, 1019–1023 (1999)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Bhattacharya, P.: On parameter-dependent connected components of gray images. Pattern Recognition 29, 1359–1368 (1996)CrossRefGoogle Scholar
  12. 12.
    Arcelli, C., Sanniti di Baja, G.: Skeletons of planar patterns. In: Kong, T.Y., Rosenfeld, A. (eds.) Topological Algorithms for Digital Image Processing, pp. 99–143. North Holland, Amsterdam (1996)CrossRefGoogle Scholar
  13. 13.
    Arcelli, C., Serino, L.: Regularization of graphlike sets in gray-tone digital images. Int. J. Pattern Recognition and Artificial Intelligence 15, 643–657 (2001)CrossRefGoogle Scholar
  14. 14.
    Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans. on PAMI 18, 1163–1173 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Carlo Arcelli
    • 1
  • Luca Serino
    • 1
  1. 1.CNRIstituto di Cibernetica ”E. Caianiello”Pozzuoli, NapoliItaly

Personalised recommendations