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    A generalized labeled multi-Bernoulli tracker for time lapse cell migration

    Access Status
    Fulltext not available
    Authors
    Kim, Du Yong
    Vo, Ba-Ngu
    Thian, A.
    Choi, Y.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kim, D.Y. and Vo, B. and Thian, A. and Choi, Y. 2017. A generalized labeled multi-Bernoulli tracker for time lapse cell migration, pp. 20-25.
    Source Title
    2017 International Conference on Control, Automation and Information Sciences, ICCAIS 2017
    DOI
    10.1109/ICCAIS.2017.8217576
    ISBN
    9781538631140
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104662
    URI
    http://hdl.handle.net/20.500.11937/66640
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. Tracking is a means to accomplish the more fundamental task of extracting relevant information about cell behavior from time-lapse microscopy data. Hence, characterizing uncertainty or confidence in the information inferred from the data is as important as the tracking of the cells. In this paper, we show that in addition to being a principled Bayesian multi-object tracking approach, the Random Finite Set (RFS) framework is capable of providing consistent characterization of uncertainty for the information inferred from the data. In particular, we use an efficient implementation of the Generalized Labeled Multi-Bernoulli (GLMB) filter to track a large number of cells in a cell migration experiment and demonstrate how to characterize uncertainty on variables inferred from the data such as cell counts, survival rate, birth rate, mean position, mean velocity using standard constructs from RFS theory.

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