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dc.contributor.authorDat Nguyen, T.
dc.contributor.authorKim, Du Yong
dc.date.accessioned2019-02-19T04:14:58Z
dc.date.available2019-02-19T04:14:58Z
dc.date.created2019-02-19T03:58:39Z
dc.date.issued2018
dc.identifier.citationDat Nguyen, T. and Kim, D.Y. 2018. On-line Tracking of Cells and Their Lineage from Time Lapse Video Data, 7th International Conference on Control Automation and Information Sciences (ICCAIS), pp. 291-296: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/73834
dc.identifier.doi10.1109/ICCAIS.2018.8570546
dc.description.abstract

© 2018 IEEE. In this paper, we propose an algorithm for tracking cells that also provides lineage information. Our approach incorporates cell spawning into the random finite set dynamic model of the cell population, which allows the Bayes multi-object filter to capture information on the cells ancestries. A generalized Labeled Multi-Bernoulli (GLMB) filter (with cell spawning model) is applied to track the cells using detections extracted from time lapse video data. Numerical results on a set of stems cells demonstrate the capability of the proposed solution to track the time-varying number of cells as well as their ancestries.

dc.publisherIEEE
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP160104662
dc.titleOn-line Tracking of Cells and Their Lineage from Time Lapse Video Data
dc.typeConference Paper
dcterms.source.startPage291
dcterms.source.endPage296
dcterms.source.titleICCAIS 2018 - 7th International Conference on Control, Automation and Information Sciences
dcterms.source.seriesICCAIS 2018 - 7th International Conference on Control, Automation and Information Sciences
dcterms.source.isbn9781538660201
dcterms.source.conference7th International Conference on Control Automation and Information Sciences (ICCAIS)
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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