Machine Vision and Applications

, Volume 27, Issue 4, pp 511–527 | Cite as

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

  • Mariella Dimiccoli
  • Jean-Pascal Jacob
  • Lionel Moisan
Original Paper


This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.


Particle detection Particle tracking  A contrario approach Time-lapse fluorescence imaging 



This work was partially funded by the French National Research Agency (ANR) under contract ANR-09-PIRI-0030-03. The first author would like to thank two anonymous reviewers for their constructive comments that greatly contributed to improving the final version of the paper.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Mariella Dimiccoli
    • 1
  • Jean-Pascal Jacob
    • 2
  • Lionel Moisan
    • 2
  1. 1.Computer Vision Center (CVC)Universitat de Barcelona (UB)BarcelonaSpain
  2. 2.Laboratory MAP5 (CNRS UMR 8145)Paris Descartes University (Paris V)ParisFrance

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