TY  - JOUR
AU  - Mariella Dimiccoli
AU  - Jean-Pascal Jacob
AU  - Lionel Moisan
PY  - 2016//
TI  - Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
T2  - MVAP
JO  - Journal of Machine Vision and Applications
SP  - 511
EP  - 527
VL  - 27
KW  - particle detection
KW  - particle tracking
KW  - a-contrario approach
KW  - 
 time-lapse fluorescence imaging
N2  - 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.
UR  - https://link.springer.com/article/10.1007/s00138-016-0757-7
L1  - http://refbase.cvc.uab.es/files/DJM2016.pdf
N1  - MILAB;
ID  - Mariella Dimiccoli2016
ER  -