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dc.contributor.authorBakurov, Illya
dc.date.accessioned2017-10-18T15:31:12Z
dc.date.available2017-10-18T15:31:12Z
dc.identifier.urihttp://hdl.handle.net/10464/13067
dc.description.abstractNon-photorealistic rendering (NPR) is concerned with the algorithm generation of images having unrealistic characteristics, for example, oil paintings or watercolour. Using genetic programming to evolve aesthetically pleasing NPR images is a relatively new approach in the art field, and in the majority of cases it takes a lot of time to generate results. With use of Cartesian genetic programming (CGP) and graphic processing units (GPUs), we can improve the performance of NPR image evolution. Evolutionary NPR can render images with interesting, and often unexpected, graphic effects. CGP provides a means to eliminate large, inefficient rendering expressions, while GPU acceleration parallelizes the calculations, which minimizes the time needed to get results. By using these tools, we can speed up the image generation process. Experiments revealed that CGP expressions are more concise, and search is more exploratory, than in tree-based approaches. Implementation of the system with GPUs showed significant speed-up.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectNPRen_US
dc.subjectCGPen_US
dc.subjectGPUen_US
dc.subjectCartesianen_US
dc.subjectGeneticen_US
dc.subjectProgrammingen_US
dc.subjectGenetic Programmingen_US
dc.subjectCartesian Genetic Programmingen_US
dc.subjectGraphic Processing Uniten_US
dc.subjectNon-photorealistic Renderingen_US
dc.subjectEvolutionary Arten_US
dc.subjectArten_US
dc.subjectEvolutionary Computationen_US
dc.titleNon-photorealistic Rendering with Cartesian Genetic Programming using Graphic Processing Unitsen_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


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