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dc.contributor.authorMohanadhas, Daniel
dc.contributor.authorSnyder, Chris
dc.contributor.authorFernandez, Amanda
dc.date.accessioned2022-08-03T17:34:27Z
dc.date.available2022-08-03T17:34:27Z
dc.date.issued7/28/2022
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1074
dc.description.abstractSemantic segmentation, the task of classifying objects in an image at a pixel level, has been done since 2012. While our method is not new, our application is. Unlike most tasks which are on clearly-defined objects, the dataset we attempt to label is like Perlin Noise: seemingly random but with clear patterns throughout. Additionally, we had a very small dataset to work with, but preliminary results show that approaches used on more standard applications also work well in this novel application.en_US
dc.description.sponsorshipThis work was supported by the National Nuclear Security Administration, Minority Serving Institutions Partnership Program DE-NA0003948.en_US
dc.language.isoen_USen_US
dc.subjectundergraduate student works
dc.titleSemantic Segmentation for Materials Classification of Nuclear Fuelsen_US
dc.typePosteren_US
dc.description.departmentComputer Scienceen_US


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