Semantic Segmentation for Materials Classification of Nuclear Fuels
| dc.contributor.author | Mohanadhas, Daniel | |
| dc.contributor.author | Snyder, Chris | |
| dc.contributor.author | Fernandez, Amanda | |
| dc.date.accessioned | 2022-08-03T17:34:27Z | |
| dc.date.available | 2022-08-03T17:34:27Z | |
| dc.date.issued | 2022-07-28 | |
| dc.description.abstract | Semantic 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.department | Computer Science | en_US |
| dc.description.sponsorship | This work was supported by the National Nuclear Security Administration, Minority Serving Institutions Partnership Program DE-NA0003948. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/1074 | |
| dc.language.iso | en_US | en_US |
| dc.subject | undergraduate student works | |
| dc.title | Semantic Segmentation for Materials Classification of Nuclear Fuels | en_US |
| dc.type | Poster | en_US |
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