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dc.contributor.authorBaniasadi, Maryam
dc.date.accessioned2013-04-18T19:20:25Z
dc.date.available2013-04-18T19:20:25Z
dc.date.issued2013-04-18
dc.identifier.urihttp://hdl.handle.net/10464/4304
dc.description.abstractThis 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.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectGenetic Programming, NPR, Evolutionary Arten_US
dc.titleGenetic Programming for Non-Photorealistic Renderingen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
dc.embargo.termsNoneen_US
refterms.dateFOA2021-08-08T02:22:54Z


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