Bayesian Multi-Object Tracking for Cell Microscopy
Access Status
Open access
Date
2021Supervisor
Ba-Ngu Vo
Ba Tuong Vo
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Science and Engineering
School
School of Electrical Engineering, Computing and Mathematical Sciences
Collection
Abstract
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.
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