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dc.contributor.authorNguyen, Tran Thien Dat
dc.contributor.supervisorBa-Ngu Voen_US
dc.contributor.supervisorBa Tuong Voen_US

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.

dc.publisherCurtin Universityen_US
dc.titleBayesian Multi-Object Tracking for Cell Microscopyen_US
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidNguyen, Tran Thien Dat [0000-0001-9185-4009]en_US

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