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dc.contributor.authorMaghoumi, Mehran
dc.date.accessioned2014-08-01T19:02:26Z
dc.date.available2014-08-01T19:02:26Z
dc.date.issued2014-08-01
dc.identifier.urihttp://hdl.handle.net/10464/5525
dc.description.abstractGenetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectObject Trackingen_US
dc.subjectGenetic Programmingen_US
dc.subjectParallel Computationen_US
dc.subjectNVIDIA CUDAen_US
dc.titleReal-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TMen_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


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