Boundary detection in piecewise homogeneous textured images

  • Stefano Casadei
  • Sanjoy Mitter
  • Pietro Perona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 588)


We address the problem of scale selection in texture analysis. Two different scale parameters, feature scale and statistical scale, are defined. Statistical scale is the size of the regions used to compute averages. We define the class of homogeneous random functions as a model of texture. A dishomogeneity function is defined and we prove that it has useful asymptotic properties in the limit of infinite statistical scale. We describe an algorithm for image partitioning which has performed well on piecewise homogeneous synthetic images. This algorithm is embedded in a redundant pyramid and does not require any ad-hoc information. It selects the optimal statistical scale at each location in the image.


Texture Analysis Thermodynamic Limit Statistical Scale Boundary Detection Image Descriptor 
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.
    H. Knuttson and G. H. Granlund. Texture analysis using two-dimensional quadrature filters. In Workshop on Computer Architecture for Pattern Analysis ans Image Database Management, pages 206–213. IEEE Computer Society, 1983.Google Scholar
  2. 2.
    M.R. Turner. Texture discrimination by gabor functions. Biol. Cybern., 55:71–82, 1986.PubMedGoogle Scholar
  3. 3.
    J. Malik and P. Perona. Preattentive texture discrimination with early vision mechanisms. Journal of the Optical Society of America — A, 7(5):923–932, 1990.Google Scholar
  4. 4.
    A.C. Bovik, M. Clark, and W.S. Geisler. Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Machine Intell., 12(1):55–73, 1990.Google Scholar
  5. 5.
    B. Julesz. Visual pattern discrimination. IRE Transactions on Information Theory IT-8, pages 84–92, 1962.Google Scholar
  6. 6.
    R. L. Kashyap and K. Eom. Texture boundary detection based on the long correlation model. IEEE transactions on Pattern Analysis and Machine Intelligence, 11:58–67, 1989.Google Scholar
  7. 7.
    D. Geman, S. Geman, C. Graffigne, and P. Dong. Boundary detection by constraint optimization. IEEE Trans. Pattern Anal. Machine Intell., 12(7):609, 1990.Google Scholar
  8. 8.
    R. Wilson and G.H. Granlund. The uncertainty principle in image processing. IEEE Trans. Pattern Anal. Machine Intell, 6(6):758–767, Nov. 1984.Google Scholar
  9. 9.
    M. Spann and R. Wilson. A quad-tree approach to image segmentation which combines statistical and spatial information. Pattern Recogn., 18:257–269, 1985.Google Scholar
  10. 10.
    S. Casadei. Multiscale image segmentation by dishomogeneity evaluation and local optimization (master thesis). Master's thesis, MIT, Cambridge, MA, May 1991.Google Scholar
  11. 11.
    S. Casadei, S. Mitter, and P. Perona. Boundary detection in piecewise homogeneous textured images (to appear). Technical Report-, MIT, Cambridge, MA,--.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Stefano Casadei
    • 1
    • 2
  • Sanjoy Mitter
    • 1
    • 2
  • Pietro Perona
    • 3
    • 4
  1. 1.Massachusetts Institute of Technology 35-308CambridgeUSA
  2. 2.Scuola Normale SuperiorePisaItaly
  3. 3.California Institute of Technology 116-81PasadenaUSA
  4. 4.Università di PadovaItaly

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