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dc.contributor.authorRezatofighi, S.
dc.contributor.authorGould, S.
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorMele, K.
dc.contributor.authorHughes, W.
dc.contributor.authorHartley, R.
dc.date.accessioned2017-08-24T02:23:38Z
dc.date.available2017-08-24T02:23:38Z
dc.date.created2017-08-23T07:21:44Z
dc.date.issued2013
dc.identifier.citationRezatofighi, S. and Gould, S. and Vo, B. and Mele, K. and Hughes, W. and Hartley, R. 2013. A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences.. Information processing in medical imaging : proceedings of the ... conference. 23: pp. 110-122.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/56341
dc.description.abstract

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.

dc.titleA multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences.
dc.typeJournal Article
dcterms.source.volume23
dcterms.source.startPage110
dcterms.source.endPage122
dcterms.source.issn1011-2499
dcterms.source.titleInformation processing in medical imaging : proceedings of the ... conference
curtin.departmentSchool of Electrical Engineering and Computing
curtin.accessStatusFulltext not available


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