%0 Journal Article
%T Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
%A Mariella Dimiccoli
%A Jean-Pascal Jacob
%A Lionel Moisan
%J Journal of Machine Vision and Applications
%D 2016
%V 27
%F Mariella Dimiccoli2016
%O MILAB;
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2735), last updated on Tue, 06 Mar 2018 10:56:11 +0100
%X 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.
%K particle detection
%K particle tracking
%K a-contrario approach
%K 
 time-lapse fluorescence imaging
%U https://link.springer.com/article/10.1007/s00138-016-0757-7
%U http://refbase.cvc.uab.es/files/DJM2016.pdf
%P 511-527