Abstract
The analysis of the dynamics of sub-cellular structures is of great interest to bio-medical research. We propose to use probabilistic tracking techniques to analyze the dynamics of stress granules (SGs) to avoid the problems of manual analysis. A crucial challenge in multitarget tracking is the association of observations to underlying targets. Rao-Blackwellized Monte Carlo Data Association (RBMCDA) based on sampling of association variables avoids the combinatorics of this association problem. We propose the use of a parametric data association prior for sampling of association variables and evaluate tracking results with regard to the impact of parameter deviations on synthetic data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kedersha N, Stoecklin G, Ayodele M, et al. Stress granules and processing bodies are dynamically linked sites of mRNP remodeling. J Cell Biol. 2005; p. 871–84.
Bar-Shalom Y, Blair WD. Multitarget-multisensor tracking: Applications and advances. Volume III. Artech House; 2000.
Reid D. An algorithm for tracking multiple targets. IEEE Trans Automatic Control. 1979; p. 843–54.
Karlsson R, Gustafsson F. Monte Carlo data association for multiple target tracking. Proc IEE. 2001;174:13/1–13/5.
Genovesio A, Olivo-Marin J. Particle tracking in 3D+t biological imaging. In: Microscopic Image Analysis for Life Science Applications. Artech House; 2008. p. 223–82.
Särkkä S, Vehtari A, Lampinen J. Rao-Blackwellized particle filter for multiple target tracking. J Inform Fusion. 2007; p. 2–15.
Greß O, Posch S. Parametric data association prior for multi-target tracking based on Rao-Blackwellized Monte Carlo Data Association. In: Proc ICCV; 2012. p. accepted.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Greß, O., Posch, S. (2012). Model Dependency of RBMCDA for Tracking Multiple Targets in Fluorescence Microscopy. In: Tolxdorff, T., Deserno, T., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2012. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28502-8_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-28502-8_44
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28501-1
Online ISBN: 978-3-642-28502-8
eBook Packages: Computer Science and Engineering (German Language)