Poisson Kalman particle filtering for tracking centrosomes in low-light 3-D confocal image sequences

Hugh Gribben, Paul Miller, Jianguo Zhang, Mark Browne

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    An automatic tracker is developed, which is capable of tracking intra-cellular features in living cells from 3-D confocal image sequences corrupted by noise. The proposed approach takes a Poisson MAP-MRF classification as an initial stage to detect objects. These are then used to update the multiple target locations generated by 3D Poisson Kalman Particle filters (PKPF). A probabilistic nearest neighbour search strategy for object association is developed to produce improved prediction of target locations. Our approach is tested in real 3D confocal image sequences with challenging illumination conditions. Results show that our Poisson Kalman particle filter approach obtains very promising results and outperforms three other tracking approaches.
    Original languageEnglish
    Title of host publicationProceedings 13th International Machine Vision and Image Processing Conference, 2009
    Subtitle of host publicationIMVIP '09
    EditorsKen Dawson-Howe, Rozenn Dahyot, Anil Kokaram, Gerard Lacey
    Place of PublicationLos Alamitos, Calif.
    PublisherIEEE
    Pages83-88
    Number of pages6
    ISBN (Electronic)9780769537962
    ISBN (Print)9781424448753
    DOIs
    Publication statusPublished - 2009
    Event13th International Machine Vision and Image Processing Conference - Dublin, Ireland
    Duration: 2 Sep 20094 Sep 2009

    Conference

    Conference13th International Machine Vision and Image Processing Conference
    CountryIreland
    CityDublin
    Period2/09/094/09/09

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  • Cite this

    Gribben, H., Miller, P., Zhang, J., & Browne, M. (2009). Poisson Kalman particle filtering for tracking centrosomes in low-light 3-D confocal image sequences. In K. Dawson-Howe, R. Dahyot, A. Kokaram, & G. Lacey (Eds.), Proceedings 13th International Machine Vision and Image Processing Conference, 2009: IMVIP '09 (pp. 83-88). IEEE. https://doi.org/10.1109/IMVIP.2009.22