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• W2152208045 abstract "This paper describes an original method to detect XFP-tagged proteins in time-lapse microscopy. Non-local measurements able to capture spatial intensity variations are incorporated within a Conditional Random Field (CRF) framework to localize the objects of interest. The minimization of the related energy is performed by a min-cut/max-flow algorithm. Furthermore, we estimate the slowly varying background at each time step. The difference between the current image and the estimated background provides new and reliable measurements for object detection. Experimental results on simulated and real data demonstrate the performance of the proposed method." @default.
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• W2152208045 date "2009-06-01" @default.
• W2152208045 modified "2023-09-25" @default.
• W2152208045 title "Conditional Random Fields for object and background estimation in fluorescence video-microscopy" @default.
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• W2152208045 doi "https://doi.org/10.1109/isbi.2009.5193152" @default.
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