Tracking human motion using auxiliary particle filters and iterated likelihood weighting
Image and Vision Computing
Predicting segmentation accuracy for biological cell images
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Tracking growing axons by particle filtering in 3d+t fluorescent two-photon microscopy images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Automated analysis of fluorescence microscopy data relies on robust segmentation and tracking algorithms for sub-cellular structures in order to generate quantitative results. The accuracy of the image processing results is, however, frequently unknown or determined a priori on synthetic benchmark data. We present a particle filter framework based on Markov Chain Monte Carlo methods and adaptive annealing. Our algorithm provides on-line per-frame estimates of the detection and tracking confidence at run time. We validate the accuracy of the estimates and apply the algorithm to tracking microtubules in mitotic yeast cells. This is based on a likelihood function that accounts for the dominant noise sources in the imaging equipment. The confidence estimates provided by the present algorithm allow on-line control of the detection and tracking quality.