@Article{MariellaDimiccoli2016,
author="Mariella Dimiccoli
and Jean-Pascal Jacob
and Lionel Moisan",
title="Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach",
journal="Journal of Machine Vision and Applications",
year="2016",
volume="27",
pages="511--527",
optkeywords="particle detection",
optkeywords="particle tracking",
optkeywords="a-contrario approach",
optkeywords="
 time-lapse fluorescence imaging",
abstract="In this work, we propose a probabilistic approach for the detection and thetracking of particles on biological images. In presence of very noised and poorquality data, particles and trajectories can be characterized by an a-contrariomodel, that estimates the probability of observing the structures of interestin random data. This approach, first introduced in the modeling of human visualperception and then successfully applied in many image processing tasks, leadsto algorithms that do not require a previous learning stage, nor a tediousparameter tuning and are very robust to noise. Comparative evaluations againsta well established baseline show that the proposed approach outperforms thestate of the art.",
optnote="MILAB;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2735), last updated on Tue, 06 Mar 2018 10:56:11 +0100",
opturl="https://link.springer.com/article/10.1007/s00138-016-0757-7",
file=":http://refbase.cvc.uab.es/files/DJM2016.pdf:PDF"
}