Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images

Single Particle Tracking (SPT) is a well known class of tools for studying the dynamics of biological macromolecules moving inside living cells. In this paper, we focus on the problem of localization and parameter estimation given a sequence of segmented images. In the standard paradigm, the locatio...

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Veröffentlicht in:	PloS one Jg. 16; H. 5; S. e0243115
Hauptverfasser:	Lin, Ye, Andersson, Sean B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	San Francisco Public Library of Science 21.05.2021 Public Library of Science (PLoS)
Schlagworte:	Algorithms Analysis Artificial neural networks Background noise Cameras CMOS Computer and Information Sciences Computer simulation Deep learning Drafting software Engineering and Technology Experimental methods Image segmentation Kalman filters Localization Machine learning Macromolecules Mechanical engineering Neural networks Noise Noise levels Parameter estimation Parameter identification Particle tracking Particle trajectories Physical properties Physical Sciences Random variables Research and Analysis Methods Systems engineering Trajectory analysis United States United States > US
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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