Edge Detection in Contaminated Images, Using Cluster Analysis

  • Héctor Allende
  • Jorge Galbiati
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this paper we present a method to detect edges in images. The method consists of using a 3x3 pixel mask to scan the image, moving it from left to right and from top to bottom, one pixel at a time. Each time it is placed on the image, an agglomerative hierarchical cluster analysis is applied to the eight outer pixels. When there is more than one cluster, it means that window is on an edge, and the central pixel is marked as an edge point. After scanning all the image, we obtain a new image showing the marked pixels around the existing edges of the image. Then a thinning algorithm is applied so that the edges are well defined. The method results to be particularly efficient when the image is contaminated. In those cases, a previous restoration method is applied.


Edge Detection Edge Point Central Pixel Pattern Recognition Letter Agglomerative Hierarchical Cluster Analysis 
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.


  1. 1.
    Allende, H., Galbiati, J.: A non-parametric to filter for digital image restoration, using cluster analysis. Pattern Recognition Letters 25, 841–847 (2004)CrossRefGoogle Scholar
  2. 2.
    Carrasco, R.C., Forcada, M.L.: A note on the Nagendraprasad-Wang-Gupta thinning algorithm. Pattern Recognition Letters 16(5), 539–541 (1995)zbMATHCrossRefGoogle Scholar
  3. 3.
    Prewitt, J.M.S.: Object enhancement and extraction. In: Rosenfeld, A., Lipkin, B.S. (eds.) Picture Processing and Psychophysics, pp. 75–149. Academic Press, New York (1970)Google Scholar
  4. 4.
    Pingle, K.K.: Line of vision perception by to computer. In: Grasselli, A. (ed.) Automatic Interpretation and Classification of Images, pp. 277–284. Academic Press, A. Grasselli, New York (1969)Google Scholar
  5. 5.
    Allende, H., Galbiati, J., Vallejos, R.: Digital Image Restoration Using Autoregressive Time Series Models. In: Proceedings of the Second Latino-American Seminar on Radar Remote Sensing, ESA-SP-434, pp. 53–59 (1998)Google Scholar
  6. 6.
    Allende, H., Galbiati, J., Vallejos, R.: Robust Image Modeling on Image Processing. Pattern Recognition Letters 22(11), 1219–1231 (2001)CrossRefGoogle Scholar
  7. 7.
    Bustos, H.O.: Robust statistics in SAR image processing. ESA-SP 407, 81–89 (1997)Google Scholar
  8. 8.
    da Costa Freitas, C., Frery, A.C., Correia, A.H.: Generalized Distributions for Multilook Polarimetric SAR Data under the Multiplicative Model. Technical Report (June 2002)Google Scholar
  9. 9.
    Kashyap, R.L., Eom, K.B.: Robust Image Techniques with an Image Restoration Application. IEEE Transactions on Acoustics, Speech and Signal Processing 36(8), 1313–1325 (1988)zbMATHCrossRefGoogle Scholar
  10. 10.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. J. Wiley, N. York (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Héctor Allende
    • 1
  • Jorge Galbiati
    • 2
  1. 1.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaíso
  2. 2.Instituto de Estadística, CasillaPontificia Universidad Católica de ValparaísoValparaísoChile

Personalised recommendations