Machine Vision and Applications

, Volume 20, Issue 1, pp 1–9 | Cite as

Automatic extraction of brushstroke orientation from paintings

POET: prevailing orientation extraction technique
  • Igor E. BerezhnoyEmail author
  • Eric O. Postma
  • H. Jaap van den Herik
Original Paper


Spatial characteristics play a major role in the human analysis of paintings. One of the main spatial characteristics is the pattern of brushstrokes. The orientation, shape, and distribution of brushstrokes are important clues for analysis. This paper focuses on the automatic extraction of the orientation of brushstrokes from digital reproductions of paintings. We present a novel technique called the (prevailing orientation extraction technique (POET)). The technique is based on a straightforward circular filter and a dedicated orientation extraction phase; it performs at a level that is undistinguishable from that of humans. From our experimental results we may conclude that POET supports the automatic extraction of the spatial distribution of oriented brushstrokes. Such an automatic extraction will aid art experts in their analysis of paintings.


Prevailing orientation extraction technique Texture Orientation extraction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arora, S., Acharya, J., Verma, A., Panigrahi, P.K.: Multilevel thresholding for image segmentation through a fast statistical recursive algorithm (2006).
  2. 2.
    Bigün, J., Granlund, G.H.: Optimal orientation detection of linear symmetry. In: Proceedings of the IEEE First International Conference on Computer Vision, pp. 433–438. London, Great Britain (1987)Google Scholar
  3. 3.
    Chetverikov, D., Haralick, R.: Texture anisotropy, symmetry, regularity: recovering structure and orientation from interaction maps. In: Proceedings of the British Machine Vision Conference, pp. 57–66 (1995)Google Scholar
  4. 4.
    Farid H. and Simoncelli E. (2004). Differentiation of multi-dimentional signals. IEEE Trans. Image Process. 13(4): 496–508 CrossRefMathSciNetGoogle Scholar
  5. 5.
    Felsberg M. and Sommer G. (2001). The monogenic signal. IEEE Trans. Signal Process. 12(49): 3136–3144 CrossRefMathSciNetGoogle Scholar
  6. 6.
    Feng, X., Milanfar, P.: Multiscale principal components analysis for image local orientation estimation. In: Proceedings of the 36th Asilomar Conference on Signals, Systems and Computers, Vol. 1, pp. 478–482. IEEE Press, Pacific Grove (2002)Google Scholar
  7. 7.
    Freeman W. and Adelson E. (1991). The design and use of steerable filters. Trans. Pattern Anal. Mach. Intell. 13: 891–906 CrossRefGoogle Scholar
  8. 8.
    Gonzalez R. and Woods R. (2002). Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs Google Scholar
  9. 9.
    Knutsson, H.: Representing local structure using tensors. In: Proceedings of Scandinavian Conference on Image Analysis (1989)Google Scholar
  10. 10.
    Lettner, M., Kammerer, P., Sablatnig, R.: Texture analysis of painted strokes. In: Proceedings 28th Workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR), Schriftenreiheder OCG, Vol. 179, pp. 269–276 (2004)Google Scholar
  11. 11.
    McMahon M. and MacLeod D. (2003). The origin of the oblique effect examined with pattern adaptation and masking. J. Vis. 3(3): 230–239 CrossRefGoogle Scholar
  12. 12.
    Pouliquen F.L., Costa J.D., Germain C. and Baylou P. (2005). A new adaptive framework for unbiased orientation estimation. Pattern Recog. 38: 2032–2046 CrossRefGoogle Scholar
  13. 13.
    van Dantzig M.M. (1973). Pictology. E.J.Brill, LeidenGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Igor E. Berezhnoy
    • 1
    Email author
  • Eric O. Postma
    • 1
  • H. Jaap van den Herik
    • 1
  1. 1.Maastricht University, MICCMaastrichtThe Netherlands

Personalised recommendations