Genetic Programming for Non-Photorealistic Rendering

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Maryam Baniasadi: Genetic Programming for Non-Photorealistic Rendering. Master Thesis & Technical Report # CS-13-08 June 2013, Brock University, Department of Computer Science.



This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi- objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

Extended Abstract


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