Tracking multiple interacting subcellular structure by sequential Monte Carlo method.
With the wide application of Green Fluorescent Proteins (GFP) in the study of live cells, there is a surging need for computer-aided analysis on the huge amount of image sequence data acquired by the advanced microscopy devices. In this paper, a framework based on Sequential Monte Carlo (SMC) is proposed for multiple interacting object tracking. The distribution of the dimension varying joint state is sampled efficiently by a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm with a novel height swap move. Experimental results were performed on synthetic and real confocal microscopy image sequences.
Wen
Quan
Q
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA. qwen@uta.edu
Luby-Phelps
Kate
K
Gao
Jean
J
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D022082:Cellular Structures / Q000166:cytology*; D049452:Green Fluorescent Proteins; D007091:Image Processing, Computer-Assisted; D049408:Luminescent Agents; D018613:Microscopy, Confocal / Q000379:methods; D009010:Monte Carlo Method; D010363:Pattern Recognition, Automated
D049408:Luminescent Agents; D049452:Green Fluorescent Proteins