Abstract
The contribution of this paper is a novel non-photorealistic rendering (NPR) algorithm for rendering real images in an impasto painterly style. We argue that figurative artworks are salience maps, and develop a novel painting algorithm that uses a genetic algorithm (GA) to search the space of possible paintings for a given image, so approaching an “optimal” artwork in which salient detail is conserved and non-salient detail is attenuated. We demonstrate the results of our technique on a wide range of images, illustrating both the improved control over level of detail due to our salience adaptive painting approach, and the benefits gained by subsequent relaxation of the painting using the GA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gombrich, E.H.: Art and Illusion. Phaidon Press Ltd., Oxford (1960)
Hall, P.M., Owen, M., Collomosse, J.P.: A trainable low-level feature detector. In: Proc. Intl. Conf. on Pattern Recognition (ICPR), vol. 1, pp. 708–711 (2004)
Hall, P., Owen, M., Collomosse, J.P.: Learning to detect low-level features. In: Proc. 15th British Machine Vision Conf. (BMVC), vol. 1, pp. 337–346 (2004)
Sziranyi, T., Tath, Z.: Random paintbrush transformation. In: Proc. 15th Intl. Conf. on Pattern Recognition (ICPR), Barcelona, vol. 3, pp. 155–158 (2000)
Hertzmann, A.: Paint by relaxation. In: Proc. Comp. Graph. Intl., pp. 47–54 (2001)
Haeberli, P.: Paint by numbers: abstract image representations. In: Proc. ACM SIGGRAPH, vol. 4, pp. 207–214 (1990)
Litwinowicz, P.: Processing images and video for an impressionist effect. In: Proc. ACM SIGGRAPH, Los Angeles, USA, pp. 407–414 (1997)
Treavett, S., Chen, M.: Statistical techniques for the automated synthesis of non-photorealistic images. In: Proc. 15th Eurographics UK Conf., pp. 201–210 (1997)
Shiraishi, M., Yamaguchi, Y.: An algorithm for automatic painterly rendering based on local source image approximation. In: Proc. ACM NPAR, pp. 53–58 (2000)
Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: Proc. ACM SIGGRAPH, pp. 453–460 (1998)
Gooch, B., Coombe, G., Shirley, P.: Artistic vision: Painterly rendering using computer vision techniques. In: Proc. ACM NPAR, pp. 83–90 (2002)
DeCarlo, D., Santella, A.: Abstracted painterly renderings using eye-tracking data. In: Proc. ACM SIGGRAPH, pp. 769–776 (2002)
Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Proc. ACM SIGGRAPH, pp. 327–340 (2001)
Walker, K.N., Cootes, T.F., Taylor, C.J.: Locating salient object features. In: Proc. 9th British Machine Vision Conf (BMVC), vol. 2, pp. 557–567 (1998)
Holland, J.: Adaptation in Natural and Artificial Systems. U. Michigan (1975)
de Jong, K.: Learning with genetic algorithms. Machine Learning 3, 121–138 (1988)
Goldberg, D.: GAs in Search, Optimization, and Machine Learning. Add. W., Reading (1989)
Collomosse, J.P.: Higher Level Techniques for the Artistic Rendering of Images and Video. PhD thesis, University of Bath, U.K. (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Collomosse, J.P., Hall, P.M. (2005). Genetic Paint: A Search for Salient Paintings. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-32003-6_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25396-9
Online ISBN: 978-3-540-32003-6
eBook Packages: Computer ScienceComputer Science (R0)