BMVC91 pp 104-110 | Cite as

Texture Boundary Detection — A Structural Approach

  • Wen Wen
  • Richard J. Fryer


Perception of different textures is caused by differences in distribution of properties of texture elements. However, in practice it is difficult to extract useful texture elements, especially from natural images in which texture elements exist at various scales. To extract texture elements of all sizes a multiscale approach is unavoidable. This paper describes a multiscale method, based on measurements in a Laplacian-of-Gaussian scale-space, to extract texture elements. Histograms are used to describe the distribution of properties of extracted texture elements in a region. The edge significance at a pixel reflects the difference in the histograms of the regions surrounding the pixel. High edge significance pixels constitute the texture boundaries. Performance of the approach is shown for various natural textured images.


Texture Feature Main Axis Natural Image Multiscale Method Texture Element 
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]
    Beck, J. Texture segmentation, in Organization and Representation in Perception, Ed. J. Beck, Erlbaum, Hillsdale, New Jersey, 1982, pp.285–317.Google Scholar
  2. [2]
    Julesz, B. and Bergen, J.R., Textons, the fundamental elements in preattentive vision and perception of textures, in Readings in Computer Vision, ed. M. A. Fischler and O. Firschein, Morgan Kaufmann Publishers, INC. 1987, pp 243-256.Google Scholar
  3. [3]
    Tomita, F., Shirai, Y. and Tsuji, S., Description of textures by a structural analysis, IEEE Tran. on Patt.Anal Machine Intell. vol PAMI-4, pp 679–698, March 1987.Google Scholar
  4. [4]
    Rearick, T.C, A texture of analysis algorithm inspired by a theory of preattentive vision, Proc. of Conf. on Computer Vision and Pattern Recognition, pp 312-317, 1985.Google Scholar
  5. [5]
    Vilnrotter, E.M., Nevatia, R and Price K.E. Structure analysis of natural textures, IEEE Trans. on Patt. Anal. and Machine Intell., Vol. PAMI-8, No.1. Jan. 1986.Google Scholar
  6. [6]
    Voorhees, H.L., and Poggio, T., Detecting textons and texture boundaries in natural images, in Proc. of First Int. Conf. on Computer Vision. London England, 8–11 June 1987, pp.250-258.Google Scholar
  7. [7]
    Blostein, D. and Ahuja, N. A multiscale region detector, Comp. Vision, Graphics and Image Proc. 45, 22–41, 1989.CrossRefGoogle Scholar
  8. [8]
    Wen, W. and Fryer, R.J. Multiscale texture element detection, in press.Google Scholar

Copyright information

© Springer-Verlag London Limited 1991

Authors and Affiliations

  • Wen Wen
    • 1
  • Richard J. Fryer
    • 1
  1. 1.Machine Perception Research Group, Department of Computer ScienceUniversity of StrathclydeGlasgowUK

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