Genetic paint: A search for salient paintings

J P Collomosse, P M Hall

Research output: Chapter or section in a book/report/conference proceedingChapter or section

28 Citations (SciVal)


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.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computing, Proceedings
Number of pages11
Publication statusPublished - 2005

Publication series

NameLecture Notes in Computer Science

Bibliographical note

ID number: ISI:000229211900044


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