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Fast and robust optical flow for time-lapse microscopy using super-voxels
Author(s) -
Fernando Amat,
Eugene W. Myers,
Philipp Keller
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts706
Subject(s) - voxel , computer science , optical flow , artificial intelligence , computer vision , background subtraction , segmentation , markov random field , pattern recognition (psychology) , image segmentation , image (mathematics) , pixel
Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research focused on 2D natural images, which are small in size and rich in edges and texture information. In contrast, 3D time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the point-spread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data.

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