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Tracking fluorescent particles in microscopy image sequences is pivotal in obtaining quantitative characterizations of the dynamical processes underlying these fluorescent structures. We have developed a probabilistic tracking approach that combines the Kalman filter with principles of the particle filter. To generate samples, we use an elliptical approximation of a Gaussian density. Each sample is...
Tracking subcellular structures displayed as `particles' in fluorescence microscopy images yields quantitative descriptions of the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles. Our approach includes a localization scheme using probabilistic data association that combines a top-down strategy driven by the Kalman filter and a bottom-up strategy...
We are investigating the dynamical relationships exhibited by virus particles via fluorescence time-lapse microscopy. To obtain a quantitative description of each particle over time, these objects are tracked. To derive an explicit characterization of each particle as well as to identify interesting transient behaviors, the intensity over time of each particle needs to be analyzed. We have developed...
Fluorescence time-lapse microscopy is a powerful technique for observing the spatial-temporal behavior of viruses. To quantitatively analyze the exhibited dynamical relationships, tracking of viruses over time is required. We have developed probabilistic approaches based on particle filters for tracking multiple virus particles in time-lapse fluorescence microscopy images. We employ a mixture of particle...
Modern developments in time-lapse microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore the spatio-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking. A quantitative comparison based on synthetic image sequences...
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