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Genetic Paint: A Search for Salient Paintings

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3449)

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.

Keywords

  • Mean Square Error
  • Source Image
  • Salient Region
  • Brush Stroke
  • 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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  • DOI: 10.1007/978-3-540-32003-6_44
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© 2005 Springer-Verlag Berlin Heidelberg

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Collomosse, J.P., Hall, P.M. (2005). Genetic Paint: A Search for Salient Paintings. In: , 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

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  • 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)