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

Hugh Gribben; Paul Miller; Jianguo Zhang; Mark Browne

doi:10.1109/IMVIP.2009.22

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 proceeding › Conference 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 language	English
Title of host publication	Proceedings 13th International Machine Vision and Image Processing Conference, 2009
Subtitle of host publication	IMVIP '09
Editors	Ken Dawson-Howe, Rozenn Dahyot, Anil Kokaram, Gerard Lacey
Place of Publication	Los Alamitos, Calif.
Publisher	IEEE
Pages	83-88
Number of pages	6
ISBN (Electronic)	9780769537962
ISBN (Print)	9781424448753
DOIs	https://doi.org/10.1109/IMVIP.2009.22
Publication status	Published - 2009
Event	13th International Machine Vision and Image Processing Conference - Dublin, Ireland Duration: 2 Sept 2009 → 4 Sept 2009

Conference

Conference	13th International Machine Vision and Image Processing Conference
Country/Territory	Ireland
City	Dublin
Period	2/09/09 → 4/09/09

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10.1109/IMVIP.2009.22

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5319272

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

Gribben, Hugh ; Miller, Paul ; Zhang, Jianguo et al. / Poisson Kalman particle filtering for tracking centrosomes in low-light 3-D confocal image sequences. Proceedings 13th International Machine Vision and Image Processing Conference, 2009: IMVIP '09. editor / Ken Dawson-Howe ; Rozenn Dahyot ; Anil Kokaram ; Gerard Lacey. Los Alamitos, Calif. : IEEE, 2009. pp. 83-88

@inproceedings{213a7b3e28d94ed9b82b939740b9adb6,

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

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.",

author = "Hugh Gribben and Paul Miller and Jianguo Zhang and Mark Browne",

year = "2009",

doi = "10.1109/IMVIP.2009.22",

language = "English",

isbn = "9781424448753",

pages = "83--88",

editor = "Ken Dawson-Howe and Rozenn Dahyot and Anil Kokaram and Lacey, {Gerard }",

booktitle = "Proceedings 13th International Machine Vision and Image Processing Conference, 2009",

publisher = "IEEE",

note = "13th International Machine Vision and Image Processing Conference ; Conference date: 02-09-2009 Through 04-09-2009",

}

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. IEEE, Los Alamitos, Calif., pp. 83-88, 13th International Machine Vision and Image Processing Conference, Dublin, Ireland, 2/09/09. https://doi.org/10.1109/IMVIP.2009.22

Poisson Kalman particle filtering for tracking centrosomes in low-light 3-D confocal image sequences. / Gribben, Hugh; Miller, Paul; Zhang, Jianguo et al.
Proceedings 13th International Machine Vision and Image Processing Conference, 2009: IMVIP '09. ed. / Ken Dawson-Howe; Rozenn Dahyot; Anil Kokaram; Gerard Lacey. Los Alamitos, Calif.: IEEE, 2009. p. 83-88.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Poisson Kalman particle filtering for tracking centrosomes in low-light 3-D confocal image sequences

AU - Gribben, Hugh

AU - Miller, Paul

AU - Zhang, Jianguo

AU - Browne, Mark

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

U2 - 10.1109/IMVIP.2009.22

DO - 10.1109/IMVIP.2009.22

M3 - Conference contribution

SN - 9781424448753

SP - 83

EP - 88

BT - Proceedings 13th International Machine Vision and Image Processing Conference, 2009

A2 - Dawson-Howe, Ken

A2 - Dahyot, Rozenn

A2 - Kokaram, Anil

A2 - Lacey, Gerard

PB - IEEE

CY - Los Alamitos, Calif.

T2 - 13th International Machine Vision and Image Processing Conference

Y2 - 2 September 2009 through 4 September 2009

ER -

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

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