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Activity Number:
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308
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305155 |
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Title:
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Dynamic Spatial Mixture Modeling and Its Application in Bayesian Tracking for Cell Fluorescent Microscopic Imaging
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Author(s):
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Chunlin Ji*+ and Mike West
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Companies:
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Duke University and Duke University
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Address:
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Department of Statistical Science, Durham, NC, 27708-0251,
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Keywords:
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spatial mixture modelling ; Dirichelet process mixture model ; particle filter ; spatial point process ; Poisson model ; cell fluorescent image
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Abstract:
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We discuss dynamic spatial mixture modeling for inhomogeneous point processes. A time varying spatial Dirichlet process Gaussian mixture model is proposed to characterize the underling dynamic of intensity of the spatial inhomogeneous point process. Consequently, the components in the mixture model are able to represent the positions of targets. A Poisson measurement model is presented for the spatial point process observations, where we assume that a single target may generate a set of spatial point observations. Bayesian inference for the intensity of a dynamic spatial inhomogeneous point process is presented in detail and we also provide the particle filter implementation of the proposed Bayesian filtering framework. Illustrative simulation examples of extended target tracking and cell fluorescent microscopic imaging tracking will be presented.
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