A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness-Reference-Cited by-同舟云学术

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A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness

Published:2012 Issue: Volume: Page:144-157
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2012
language:
Short-container-title:

Author:

Kausler Bernhard X.,Schiegg Martin,Andres Bjoern,Lindner Martin,Koethe Ullrich,Leitte Heike,Wittbrodt Jochen,Hufnagel Lars,Hamprecht Fred A.

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-33712-3_11

Reference23 articles.

1. Lecture Notes in Computer Science;B. Andres,2010

2. Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)

3. Bise, R., Yin, Z., Kanade, T.: Reliable cell tracking by global data association. In: IEEE International Symposium on Biomedical Imaging, ISBI 2011 (2011)

4. Blake, A., Kohli, P., Rother, C. (eds.): Markov Random Fields for Vision and Image Processing. MIT Press (2011)

5. Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)

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1. Graph Neural Network for Cell Tracking in Microscopy Videos;Lecture Notes in Computer Science;2022

2. A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction;PLOS ONE;2021-09-07

3. A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction;2021-03-16

4. SCOTT: Shape-Location Combined Tracking with Optimal Transport;SIAM Journal on Mathematics of Data Science;2020-01

5. Tracing cell lineages in videos of lens-free microscopy;Medical Image Analysis;2018-08

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