Iterative Brush Path Extraction Algorithm for Aiding Flock Brush Simulation of Stroke-Based Painterly Rendering

  • Tieta Putri
  • Ramakrishnan Mukundan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9596)


Painterly algorithms form an important part of non-photorealistic rendering (NPR) techniques where the primary aim is to incorporate expressive and stylistic qualities in the output. Extraction, representation and analysis of brush stroke parameters are essential for mapping artistic styles in stroke based rendering (SBR) applications. In this paper, we present a novel iterative method for extracting brush stroke regions and paths for aiding a particle swarm based SBR process. The algorithm and its implementation aspects are discussed in detail. Experimental results are presented showing the painterly rendering of input images and the extracted brush paths.


Computational intelligence Non-photorealistic rendering Brush stroke extraction Painterly rendering Flock simulation Autonomous agents Swarm intelligence 


  1. 1.
    Gooch, B., Gooch, A.: Non-photorealistic Rendering. AK Peters Ltd., Natick (2001)zbMATHGoogle Scholar
  2. 2.
    Hertzmann, A.: A Survey of Stroke-based rendering. In: IEEE Computer Graphics and Application, vol. 4, pp. 70–81. IEEE Press, New York (2003)Google Scholar
  3. 3.
    Whitted, T.: Anti-aliased line drawing using brush extrusion. Assoc. Comput. Machi. Spec. Interest Group Comput. Graph. Interact. Techn. Comput. Graph. 17(3), 151–156 (1983). ACMGoogle Scholar
  4. 4.
    Strassman, S.: Hairy brush. Assoc. Comput. Machi. Spec. Interest Group Comput. Graph. Interact. Techn. Comput. Graph. 20(4), 225–232 (1986)Google Scholar
  5. 5.
    Pham, B.: Expressive brush strokes. Comput. Vis. Graph. Image Process. Graph. Models Image Process. 53(1), 1–6 (1991). ElsevierzbMATHGoogle Scholar
  6. 6.
    Pudet, T.: Real time fitting of hand-sketched pressure brushstrokes. Comput. Graph. Forum 13(3), 205–220 (1994). Wiley Online LibraryCrossRefGoogle Scholar
  7. 7.
    Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: 25th Annual Conference on Computer graphics and Interactive Techniques 1998, pp. 453–460. ACM (1998)Google Scholar
  8. 8.
    Huang, H.E., Lim, M.H., Chen, X., Ho, C.S.: Interactive GA flock brush for non-photorealistic rendering. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds.) SEAL 2012. LNCS, vol. 7673, pp. 480–490. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Li, J., Yao, L., Hendriks, E., Wang, J.Z.: Rhythmic brushstrokes distinguish Van Gogh from his contemporaries: findings via automated brushstroke extraction. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1159–1176 (2012). IEEECrossRefGoogle Scholar
  10. 10.
    Meer, P., Georgescu, B.: Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Machine Intell. 23(12), 1351–1365 (2001). IEEECrossRefGoogle Scholar
  11. 11.
    Johnson Jr., C.R., Hendriks, E., Berezhnoy, I.J., Brevdo, E., Hughes, S.M., Daubechies, I., Li, J., Postma, E., Wang, J.Z.: Image processing for artist identification. IEEE Sig. Process. Mag. 25(4), 37–48 (2008). IEEECrossRefGoogle Scholar
  12. 12.
    Berezhnoy, I.E., Postma, E., Van Den Herik, H.J.: Automatic extraction of brushstroke orientation from paintings. Mach. Vis. Appl. 20(1), 1–9 (2009). SpringerCrossRefzbMATHGoogle Scholar
  13. 13.
    Seo, S., Lee, H.: Pixel based stroke generation for painterly effect using maximum homogeneity neighbor filter. Multimedia Tools Appl. 74(10), 3317–3328 (2015). SpringerMathSciNetCrossRefGoogle Scholar
  14. 14.
    Gooch, B., Coombe, G., Shirley, P.: Artistic vision: painterly rendering using computer vision techniques. In: 2nd International Symposium on Non-photorealistic Animation and Rendering 2002, pp. 83-ff. ACM (2002)Google Scholar
  15. 15.
    Obaid, M., Mukundan, R., Bell, T.: Enhancement of moment based painterly rendering using connected components. In: International Conference on Computer Graphics, Imaging and Visualization 2006, Sydney, pp 378–383. IEEE (2006)Google Scholar
  16. 16.
    Reinhard, E., Khan, E.A., Akyuz, A.O., Johnson, G.: Colour Imaging: Fundamentals and Applications. AK Peters Ltd, Wellesley (2008)Google Scholar
  17. 17.
    Connolly, C., Fleiss, T.: A study of efficiency and accuracy in the transformation from RGB to CIELAB colour space. IEEE Trans. Image Process. 6(7), 1046–1048 (1997). IEEECrossRefGoogle Scholar
  18. 18.
    Huang, H. E., Ong, Y. S., Chen, X.: Autonomous flock brush for non-photorealistic rendering. In: IEEE Congress on Evolutionary Computation 2012, pp. 1–8. IEEE (2012)Google Scholar
  19. 19.
    Kennedy, J., Kennedy, J.F., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  20. 20.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 4(21), 25–34 (1987). ACMCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of CanterburyChristchurchNew Zealand

Personalised recommendations