A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences.
Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, man...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/56341 |
| _version_ | 1848759850156163072 |
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| author | Rezatofighi, S. Gould, S. Vo, Ba-Ngu Mele, K. Hughes, W. Hartley, R. |
| author_facet | Rezatofighi, S. Gould, S. Vo, Ba-Ngu Mele, K. Hughes, W. Hartley, R. |
| author_sort | Rezatofighi, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters. |
| first_indexed | 2025-11-14T10:06:26Z |
| format | Journal Article |
| id | curtin-20.500.11937-56341 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:06:26Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-563412017-08-24T02:23:38Z A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. Rezatofighi, S. Gould, S. Vo, Ba-Ngu Mele, K. Hughes, W. Hartley, R. Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters. 2013 Journal Article http://hdl.handle.net/20.500.11937/56341 restricted |
| spellingShingle | Rezatofighi, S. Gould, S. Vo, Ba-Ngu Mele, K. Hughes, W. Hartley, R. A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. |
| title | A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. |
| title_full | A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. |
| title_fullStr | A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. |
| title_full_unstemmed | A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. |
| title_short | A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. |
| title_sort | multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences. |
| url | http://hdl.handle.net/20.500.11937/56341 |