Stroke Based Painterly Rendering

Part of the Computational Imaging and Vision book series (CIVI, volume 42)


Many traditional art forms are produced by an artist sequentially placing a set of marks, such as brush strokes, on a canvas. Stroke based Rendering (SBR) is inspired by this process, and underpins many early and contemporary Artistic Stylization algorithms. This chapter outlines the origins of SBR, and describes key algorithms for placement of brush strokes to create painterly renderings from source images. The chapter explores both local greedy, and global optimization based approaches to stroke placement. The issue of creative control in SBR is also briefly discussed.


Source Image Brush Stroke Artistic Stylization Stroke Size Line Integral Convolution 
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.
    Amini, A., Weymouth, T., Jain, T.: Using dynamic programming for solving variational problems in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 9(12), 855–867 (1990) CrossRefGoogle Scholar
  2. 2.
    Collomosse, J.P.: Higher level techniques for the artistic rendering of images and video. Ph.D. thesis, University of Bath, UK (2004) Google Scholar
  3. 3.
    Collomosse, J.: Supervised genetic search for parameter selection in painterly rendering. In: Proceedings EvoMUSART (LNCS), vol. 3907, pp. 599–610. Springer, Berlin (2006) Google Scholar
  4. 4.
    Collomosse, J., Hall, P.M.: Painterly rendering using image salience. In: Proc. Eurographics UK, pp. 122–128 (2002) CrossRefGoogle Scholar
  5. 5.
    Collomosse, J., Hall, P.M.: Cubist style rendering from photographs. IEEE Trans. Vis. Comput. Graph. 4(9), 443–453 (2003) CrossRefGoogle Scholar
  6. 6.
    Collomosse, J.P., Hall, P.M.: Genetic Paint: A Search for Salient Paintings. In: Proceedings of EvoWorkshops. Lecture Notes in Computer Science, vol. 3449, pp. 437–447. Springer, Berlin (2005) Google Scholar
  7. 7.
    Farin, G.: Curves and Surfaces for CAGD: A Practical Guide, 5th edn. Morgan Kaufmann, San Francisco (2002) Google Scholar
  8. 8.
    Haeberli, P.E.: Paint by numbers: abstract image representations. In: Computer Graphics (Proceedings of SIGGRAPH 90), pp. 207–214 (1990) Google Scholar
  9. 9.
    Haggerty, P.: Almost automatic computer painting. IEEE Comput. Graph. Appl. 11(6), 11–12 (1991) Google Scholar
  10. 10.
    Hall, P.M., Owen, M.J., Collomosse, J.P.: A trainable low-level feature detector. In: Proceedings Intl. Conference on Pattern Recognition (ICPR), vol. 1, pp. 708–711. IEEE Press, New York (2004) CrossRefGoogle Scholar
  11. 11.
    Hays, J., Essa, I.: Image and video based painterly animation. In: Proc. NPAR, pp. 113–120 (2004) CrossRefGoogle Scholar
  12. 12.
    Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: Proceedings of SIGGRAPH 98, Computer Graphics Proceedings. Annual Conference Series, pp. 453–460 (1998) Google Scholar
  13. 13.
    Hertzmann, A., Perlin, K.: Painterly rendering for video and interaction. In: NPAR 2000: First International Symposium on Non Photorealistic Animation and Rendering, pp. 7–12 (2000) CrossRefGoogle Scholar
  14. 14.
    Holland, J.: Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975) Google Scholar
  15. 15.
    Huang, H., Fu, T.N., Li, C.F.: Painterly rendering with content-dependent natural paint strokes. Vis. Comput. 27(9), 861–871 (2011). doi: 10.1007/s00371-011-0596-5 CrossRefGoogle Scholar
  16. 16.
    Kagaya, M., Brendel, W., Deng, Q., Kesterson, T., Todorovic, S., Neill, P.J., Zhang, E.: Video painting with space-time-varying style parameters. IEEE Trans. Vis. Comput. Graph. 17(1), 74–87 (2011) CrossRefGoogle Scholar
  17. 17.
    Litwinowicz, P.: Processing images and video for an impressionist effect. In: Proceedings of SIGGRAPH 97, Computer Graphics Proceedings. Annual Conference Series, pp. 407–414 (1997) CrossRefGoogle Scholar
  18. 18.
    O’Donovan, P., Hertzmann, A.: AniPaint: interactive painterly animation from video. IEEE Trans. Vis. Comput. Graph. 18(3), 475–487 (2012) CrossRefGoogle Scholar
  19. 19.
    Santella, A., DeCarlo, D.: Visual interest and NPR: an evaluation and manifesto. In: Proc. NPAR, pp. 71–150 (2004) CrossRefGoogle Scholar
  20. 20.
    Shiraishi, M., Yamaguchi, Y.: An algorithm for automatic painterly rendering based on local source image approximation. In: NPAR 2000: First International Symposium on Non Photorealistic Animation and Rendering, pp. 53–58 (2000) CrossRefGoogle Scholar
  21. 21.
    Shugrina, M., Betke, M., Collomosse, J.P.: Empathic painting: interactive stylization through observed emotional state. In: NPAR 06, pp. 87–96. ACM, New York (2006). doi: 10.1145/1124728.1124744 CrossRefGoogle Scholar
  22. 22.
    Szirányi, T., Tóth, Z., Figueiredo, M., Zerubia, J., Jain, A.: Optimization of paintbrush rendering of images by dynamic MCMC methods. In: Proc. EMMCVPR, pp. 201–215 (2001) Google Scholar
  23. 23.
    Treavett, S.M.F., Chen, M.: Statistical techniques for the automated synthesis of non-photorealistic images. In: Proc. EGUK, pp. 201–210 (1997) Google Scholar
  24. 24.
    Willats, J., Durand, F.: Defining pictorial style: lessons from linguistics and computer graphics. Axiomathes 15(3), 319–351 (2005) CrossRefGoogle Scholar
  25. 25.
    Zeng, K., Zhao, M., Xiong, C., Zhu, S.C.: From image parsing to painterly rendering. ACM Trans. Graph. 29(1), 2:1–2:11 (2009) CrossRefGoogle Scholar
  26. 26.
    Zhang, E., Hays, J., Turk, G.: Interactive tensor field design and visualization on surfaces. IEEE Trans. Vis. Comput. Graph. 13(1), 94–107 (2007) CrossRefGoogle Scholar
  27. 27.
    Zhao, M., Zhu, S.C.: Sisley the abstract painter. In: NPAR’10: Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering, pp. 99–107. ACM, New York (2010). doi: 10.1145/1809939.1809951 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.IRITUniversité de ToulouseToulouse CEDEX 9France
  2. 2.Centre for Vision Speech and Signal ProcessingUniversity of SurreyGuildfordUK

Personalised recommendations