Evolutionary Search for the Artistic Rendering of Photographs

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Collomosse, John: Evolutionary Search for the Artistic Rendering of Photographs. In: Romero, Juan; Machado, Penousal: The Art of Artificial Evolution. Springer, Berlin, 2007, S. 39-62.




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.

Extended Abstract


booktitle={The Art of Artificial Evolution},
series={Natural Computing Series},
editor={Romero, Juan and Machado, Penousal},
title={Evolutionary Search for the Artistic Rendering of Photographs},
url={http://dx.doi.org/10.1007/978-3-540-72877-1_2 http://de.evo-art.org/index.php?title=Evolutionary_Search_for_the_Artistic_Rendering_of_Photographs},
publisher={Springer Berlin Heidelberg},
author={Collomosse, JohnP.},

Used References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


Full Text


intern file

Sonstige Links