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Evolutionary Search for the Artistic Rendering of Photographs

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Part of the Natural Computing Series book series (NCS)

Summary

This chapter explores algorithms for the artistic stylization (transformation) of photographs into digital artwork, complementing techniques discussed so far in this book that focus on image generation. Most artistic stylization algorithms operate by placing atomic rendering primitives “strokes” on a virtual canvas, guided by automated artistic heuristics. In many cases the stroke placement process can be phrased as an optimization problem, demanding guided exploration of a high dimensional and turbulent search space to produce aesthetically pleasing renderings. Evolutionary search algorithms can offer attractive solutions to such problems.

This chapter begins with a brief review of artistic stylization algorithms, in particular algorithms for producing painterly renderings from two-dimensional sources. It then discusses how genetic algorithms may be harnessed both to increase control over level of detail when painting (so improving aesthetics) and to enhance usability of parameterized painterly rendering algorithms.

Keywords

  • Source Image
  • Salient Region
  • Brush Stroke
  • Genetic Algorithm Search
  • Salience Measure

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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Reference

  1. Salisbury, M.P., Anderson, S.E., Barzel, R., Salesin, D.H. (1994). Interactive pen-and-ink illustration. In: Proc. ACM SIGGRAPH. Florida, USA, 101–108

    Google Scholar 

  2. Salisbury, M.P., Wong, M.T., Hughes, J.F., Salesin, D.H. (1997). Orientable textures for image-based pen-and-ink illustration. In: Proc. ACM SIGGRAPH. Los Angeles, USA, 401–406

    Google Scholar 

  3. Litwinowicz, P. (1997). Processing images and video for an impressionist effect. In: Proc. ACM SIGGRAPH. Los Angeles, USA, 407–414

    Google Scholar 

  4. Hertzmann, A. (1998). Painterly rendering with curved brush strokes of multiple sizes. In: Proc. ACM SIGGRAPH, 453–460

    Google Scholar 

  5. Haeberli, P. (1990). Paint by numbers: Abstract image representations. In: Proc. ACM SIGGRAPH. Vol. 4, 207–214, Figs. 1 and 4. http://doi.acm.org/10.1145/97879.97902

    Google Scholar 

  6. Haggerty, M. (1991). Almost automatic computer painting. IEEE Computer Graphics and Applications, 11(6): 11–12

    Google Scholar 

  7. Treavett, S., Chen, M. (1997). Statistical techniques for the automated synthesis of non-photorealistic images. In: Proc. Eurographics UK, 201–210

    Google Scholar 

  8. Shiraishi, M., Yamaguchi, Y. (2000). An algorithm for automatic painterly rendering based on local source image approximation. In: Proc. ACM NPAR

    Google Scholar 

  9. Curtis, C., Anderson, S., Seims, J., Fleischer, K., Salesin, D.H. (1997). Computer-generated watercolor. In: Proc. ACM SIGGRAPH, 421–430

    Google Scholar 

  10. Hertzmann, A., Jacobs, C., Oliver, N., Curless, B., Salesin, D.H. (2001). Image analogies. In: Proc. ACM SIGRGAPH, 327–340

    Google Scholar 

  11. Hertzmann, A. (2001). Paint by relaxation. In: Proc. Computer Graphics Intl. (CGI), 47–54

    Google Scholar 

  12. Collomosse, J.P., Hall, P.M. (2005). Genetic paint: A search for salient paintings. In: Applications of Evolutionary Computing, EvoWorkshops 2004. Vol. 3449 of LNCS, 437–447

    Google Scholar 

  13. Gooch, B., Coombe, G., Shirley, P. (2002). Artistic vision: Painterly rendering using computer vision techniques. In: Proc. ACM NPAR, 83–90

    Google Scholar 

  14. DeCarlo, D., Santella, A. (2002). Abstracted painterly renderings using eye-tracking data. In: Proc. ACM SIGGRAPH, 769–776

    Google Scholar 

  15. Meier, B. (1996). Painterly rendering for animation. In: Proc. ACM SIGGRAPH, 447–484

    Google Scholar 

  16. Hertzmann, A., Perlin, K. (2000). Painterly rendering for video and interaction. In: Proc. ACM NPAR, 7–12

    Google Scholar 

  17. Wang, J., Xu, Y., Shum, H.Y., Cohen, M. (2004). Video tooning. In: Proc. ACM SIGGRAPH, 574–583

    Google Scholar 

  18. Collomosse, J.P., Rowntree, D., Hall, P.M. (2005). Stroke surfaces: Temporally coherent non-photorealistic animations from video. IEEE Trans. Visualization and Comp. Graphics, 11(5): 540–549

    CrossRef  Google Scholar 

  19. Kass, M., Witkin, A., Terzopoulos, D. (1987). Active contour models. Intl. Journal of Computer Vision (IJCV), 1(4): 321–331

    CrossRef  Google Scholar 

  20. Gombrich, E.H. (1960). Art and Illusion. Phaidon Press Ltd.. Oxford

    Google Scholar 

  21. Collomosse, J.P., Hall, P.M. (2002). Painterly rendering using image salience. In: Proc. Eurographics UK, 122–128

    Google Scholar 

  22. Holland, J. (1975). Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University Michigan Press

    Google Scholar 

  23. de Jong, K. (1988). Learning with genetic algorithms. Machine Learning, 3: 121–138

    CrossRef  Google Scholar 

  24. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley. Reading, MA ISBN: 0-201-15767-5.

    Google Scholar 

  25. Hall, P.M., Owen, M., Collomosse, J.P. (2004). A trainable low-level feature detector. In: Proc. Intl. Conf. on Pattern Recognition (ICPR). Vol. 1, 708–711

    CrossRef  Google Scholar 

  26. Walker, K.N., Cootes, T.F., Taylor, C.J. (1998). Locating salient object features. In: Proc. British Machine Vision Conf. (BMVC). Vol. 2, 557–567

    Google Scholar 

  27. Agarwala, A. (2002). Snaketoonz: A semi-automatic approach to creating cel animation from video. In: Proc. ACM NPAR, 139–147

    Google Scholar 

  28. Collomosse, J.P. (2006). Supervised genetic search for parameter selection in painterly rendering. In: Applications of Evolutionary Computing, EvoWorkshops 2006. Vol. 3907, 599–610

    CrossRef  Google Scholar 

  29. Christoudias, C., Georgescu, B., Meer, P. (2002). Synergism in low level vision. In: Intl. Conf. on Pattern Recog. (ICPR). Vol. 4, 150–155

    Google Scholar 

  30. Hertzmann, A. (2002). Fast paint texture. In: Proc. ACM NPAR, 91–96

    Google Scholar 

  31. Russell, J.A. (1997). Reading emotion from and into faces: Rsurrecting a dimensional-contextual perspective. In Russel, J.A., Fernández-Dols, J.M., eds.: The Psychology of Facial Expression. Cambridge University Press, 295–320

    Google Scholar 

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Collomosse, J.P. (2008). Evolutionary Search for the Artistic Rendering of Photographs. In: Romero, J., Machado, P. (eds) The Art of Artificial Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72877-1_2

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  • DOI: https://doi.org/10.1007/978-3-540-72877-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

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