Bayesian Multi-Object Tracking for Cell Microscopy
Cell tracking is an essential tool for studying how cells behave and divide under different conditions. This thesis proposes new approaches to track cells and their lineages using random finite set, which allows the tracking errors to be statistically quantified. Additionally, this thesis also explo...
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| Format: | Thesis |
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Curtin University
2021
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| Online Access: | http://hdl.handle.net/20.500.11937/86947 |
| _version_ | 1848764887027679232 |
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| author | Nguyen, Tran Thien Dat |
| author_facet | Nguyen, Tran Thien Dat |
| author_sort | Nguyen, Tran Thien Dat |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Cell tracking is an essential tool for studying how cells behave and divide under different conditions. This thesis proposes new approaches to track cells and their lineages using random finite set, which allows the tracking errors to be statistically quantified. Additionally, this thesis also explores criteria to rank performance of basic vision task algorithms (e.g., object detection, instance-level segmentation, and tracking), which have not been received proportionate attention from the scientific community. |
| first_indexed | 2025-11-14T11:26:29Z |
| format | Thesis |
| id | curtin-20.500.11937-86947 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:26:29Z |
| publishDate | 2021 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-869472021-12-14T07:00:21Z Bayesian Multi-Object Tracking for Cell Microscopy Nguyen, Tran Thien Dat Cell tracking is an essential tool for studying how cells behave and divide under different conditions. This thesis proposes new approaches to track cells and their lineages using random finite set, which allows the tracking errors to be statistically quantified. Additionally, this thesis also explores criteria to rank performance of basic vision task algorithms (e.g., object detection, instance-level segmentation, and tracking), which have not been received proportionate attention from the scientific community. 2021 Thesis http://hdl.handle.net/20.500.11937/86947 Curtin University fulltext |
| spellingShingle | Nguyen, Tran Thien Dat Bayesian Multi-Object Tracking for Cell Microscopy |
| title | Bayesian Multi-Object Tracking for Cell Microscopy |
| title_full | Bayesian Multi-Object Tracking for Cell Microscopy |
| title_fullStr | Bayesian Multi-Object Tracking for Cell Microscopy |
| title_full_unstemmed | Bayesian Multi-Object Tracking for Cell Microscopy |
| title_short | Bayesian Multi-Object Tracking for Cell Microscopy |
| title_sort | bayesian multi-object tracking for cell microscopy |
| url | http://hdl.handle.net/20.500.11937/86947 |