TY - GEN
T1 - Reliable tracking of large scale dense antiparallel particle motion for fluorescence live cell imaging
AU - Yang, Ge
AU - Matov, Alexandre
AU - Danuser, Gaudenz
N1 - Publisher Copyright:
© 2005 IEEE Computer Society. All rights reserved.
PY - 2005
Y1 - 2005
N2 - This paper presents a technique that reliably tracks large numbers of particles undergoing dense antiparallel motion and frequent appearance and disappearance. Such techniques are essential to many applications of fluorescence cellular and molecular imaging for automated quantitative analysis of dynamic cellular functions. The basic tracking algorithm of this technique integrates motion models at particle, local and global levels. It establishes correspondence between particles based on state similarity and resolves correspondence conflicts using optimal graph assignment. A statistical and robust approach for algorithm parameter setting is developed through establishing the equivalence of the algorithm to a Kalman-filtering based tracker under assumptions that are biologically supported. Online track initiation and propagation depend critically on computing the global vector field of particle flow using a new optimal-flow minimum-cost graph algorithm. Vector field denoising and interpolation are performed using anisotropic filtering after clustering. The technique has been experimentally verified and successfully applied to the tracking of Fluorescent Speckle Microscopy images of live cells.
AB - This paper presents a technique that reliably tracks large numbers of particles undergoing dense antiparallel motion and frequent appearance and disappearance. Such techniques are essential to many applications of fluorescence cellular and molecular imaging for automated quantitative analysis of dynamic cellular functions. The basic tracking algorithm of this technique integrates motion models at particle, local and global levels. It establishes correspondence between particles based on state similarity and resolves correspondence conflicts using optimal graph assignment. A statistical and robust approach for algorithm parameter setting is developed through establishing the equivalence of the algorithm to a Kalman-filtering based tracker under assumptions that are biologically supported. Online track initiation and propagation depend critically on computing the global vector field of particle flow using a new optimal-flow minimum-cost graph algorithm. Vector field denoising and interpolation are performed using anisotropic filtering after clustering. The technique has been experimentally verified and successfully applied to the tracking of Fluorescent Speckle Microscopy images of live cells.
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U2 - 10.1109/CVPR.2005.519
DO - 10.1109/CVPR.2005.519
M3 - Conference contribution
AN - SCOPUS:84897877342
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
BT - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
PB - IEEE Computer Society
T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
Y2 - 21 September 2005 through 23 September 2005
ER -