Glass Patterns and Artistic Imaging

  • Giuseppe Papari
  • Nicolai Petkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


The theory of Glass patterns naturally combines three essential aspects of painterly artworks: perception, randomness, and geometric structure. Therefore, it seems a suitable framework for the development of mathematical models of the visual properties that distinguish paintings from photographic images. With this contribution, we introduce a simple mathematical operator which transfers the microstructure of a Glass pattern to an input image, and we show that its output is perceptually similar to a painting. An efficient implementation is presented. Unlike most of the existing techniques for unsupervised painterly rendering, the proposed approach does not introduce ’magic numbers’ and has a nice and compact mathematical description, which makes it suitable for further theoretical analysis. Experimental results on a broad range of input images validate the effectiveness of the proposed method in terms of lack of undesired artifacts, which are present with other existing methods, and easy interpretability of the input parameters.


Input Image Object Contour Isotropic Scaling Brush Stroke Glass Pattern 
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.
    Litwinowicz, P.: Processing images and video for an impressionist effect. In: Siggraph, pp. 407–414 (1997)Google Scholar
  2. 2.
    Shiraishi, M., Yamaguchi, Y.: An algorithm for automatic painterly rendering based on local source image approximation. In: NPAR, pp. 53–58 (2000)Google Scholar
  3. 3.
    Li, N., Huang, Z.: Zhiyong Huang. Feature-guided painterly image rendering. In: ICIP, pp. 653–656 (2002)Google Scholar
  4. 4.
    Haeberli, P.: Paint by numbers: Abstract image representations. Computer Graphics 24(4), 207–214 (1990)CrossRefGoogle Scholar
  5. 5.
    De Carlo, D., Santella, A.: Stylization and abstraction of photographs. ACM Transactions on Graphics (TOG) 21(3), 769–776 (2002)Google Scholar
  6. 6.
    Gooch, B., Coombe, G., Shirley, P.: Artistic vision: painterly rendering using computer vision techniques. In: NPAR, pp. 83–90 (2002)Google Scholar
  7. 7.
    Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: Siggraph, pp. 453–460 (1998)Google Scholar
  8. 8.
    Kasao, A., Miyata, K.: Algorithmic painter: a NPR method to generate various styles of painting. The Visual Computer 22(1), 14–27 (2006)CrossRefGoogle Scholar
  9. 9.
    Collomosse, J.P., Hall, P.M.: Salience-adaptive painterly rendering using genetic search. International Journal on Artificial Intelligence Tools 15(4), 551–575 (2006)CrossRefGoogle Scholar
  10. 10.
    Santella, A., DeCarlo, D.: Abstracted painterly renderings using eye-tracking data. In: Spencer, S.N. (ed.) NPAR, pp. 75–82. ACM Press, New York (2002)Google Scholar
  11. 11.
    Hertzmann, A.: A survey of stroke-based rendering. IEEE Computer Graphics and Applications 23(4), 70–81 (2003)CrossRefGoogle Scholar
  12. 12.
    Olsen, S.C., Maxwell, B.A., Gooch, B.: Interactive vector fields for painterly rendering. In: Proceedings of Graphics Interface 2005, pp. 241–247 (2005)Google Scholar
  13. 13.
    Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. University of California Press (1974)Google Scholar
  14. 14.
    Arnheim, R.: Toward a psychology of art. University of California Press, Berkeley (1966)Google Scholar
  15. 15.
    Ogden, R.M.: Naive geometry in the psychology of art. American Journal of Psychology 49(2), 198–216 (1937)CrossRefGoogle Scholar
  16. 16.
    Smith, M.A., Bair, W., Movshon, J.A.: Signals in macaque striate cortical neurons that support the perception of Glass patterns. Journal of Neuroscience 22(18), 8334–8345 (2002)Google Scholar
  17. 17.
    Smith, M.A., Kohn, A., Movshon, J.A.: Glass pattern responses in macaque V2 neurons. Journal of Vision 7(3), 5 (2007)CrossRefGoogle Scholar
  18. 18.
    Yen, S.C., Finkel, L.H.: Extraction of perceptually salient contours by striate cortical networks. Vis. Res. 38(5), 719–741 (1998)CrossRefGoogle Scholar
  19. 19.
    Li, Z.: A neural model of contour integration in the primary visual cortex. Neur. Comp. 10(4), 903–940 (1998)CrossRefGoogle Scholar
  20. 20.
    Maloney, R.K., Mitchison, G.J., Barlow, H.B.: Limit to the detection of Glass patterns in the presence of noise. J. Opt. Soc. Am. A 4(12), 2336–2341 (1987)CrossRefGoogle Scholar
  21. 21.
    Wilson, H.R., Wilkinson, F.: Detection of global structure in Glass patterns: implications for form vision. Vision Research 38(19), 2933–2947 (1998)CrossRefGoogle Scholar
  22. 22.
    Seu, L., Ferrera, V.P.: Detection thresholds for spiral Glass patterns. Vision Research 41(28), 3785–3790 (2001)CrossRefGoogle Scholar
  23. 23.
    Dakin, S.C.: The detection of structure in Glass patterns: Psychophysics and computational models. Vision Research 37(16), 2227–2246 (1997)CrossRefGoogle Scholar
  24. 24.
    Prazdny, K.: Psychophysical and computational studies of random-dot moire patterns. Spatial Vision 1(3), 231–242 (1986)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Phillips, T.H., Rosenfeld, A.: A simplified method of detecting structure in Glass patterns. Pattern Recognition Letters 4(3), 213–217 (1986)CrossRefGoogle Scholar
  26. 26.
    Wilson, J.A., Switkes, E., De Valois, R.L.: Glass pattern studies of local and global processing of contrast variations. Vision Research 44(22), 2629–2641 (2004)CrossRefGoogle Scholar
  27. 27.
    Amidror, I.: Unified approach for the explanation of stochastic and periodic moirés. Journal of Electronic Imaging 12(4), 669–681 (2003)CrossRefGoogle Scholar
  28. 28.
    Amidror, I.: Glass patterns as moiré effects: new surprising results. Optics Letters 28(1), 7–9 (2003)CrossRefGoogle Scholar
  29. 29.
    Amidror, I.: The theory of the Moiré phenomenon, volume 1: periodic layers. Springer, Heidelberg (2000)CrossRefzbMATHGoogle Scholar
  30. 30.
    Glass, L.: Looking at dots. Math. Intell. 24(4), 37–43 (2002)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Amidror, I.: Dot trajectories in the superposition of random screens: analysis and synthesis. Journal of the Optical Society of America A 21(8), 1472–1487 (2004)CrossRefGoogle Scholar
  32. 32.
    Cumani, A.: Edge detection in multispectral images. CVGIP 53(1), 40–51 (1991)zbMATHGoogle Scholar
  33. 33.
    Papari, G., Petkov, N.: Continous glass patterns for painterly rendering. IEEE Transactions on Image Processing (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Giuseppe Papari
    • 1
  • Nicolai Petkov
    • 1
  1. 1.Institute of Mathematics and Computing ScienceUniversity of GroningenNetherlands

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