Conditional random fields for object and background estimation in fluorescence video-microscopy

Abstract

International audienceThis paper describes an original method to detect XFP-tagged pro- teins in time-lapse microscopy. Non-local measurements able to capture spatial intensity variations are incorporated within a Con- ditional 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 re- liable measurements for object detection. Experimental results on simulated and real data demonstrate the performance of the proposed method