MRF Modeling for Optical Flow Computation from Multi-Structure Objects

Prinet, V.; Cassisa, Cyril; Tang, Ff

doi:10.1109/icip.2006.312746

Cited by 3 publications

(2 citation statements)

References 13 publications

(12 reference statements)

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“…However, as shown in Table 3, rectangular grids do not adapt well to sparse signals and degrade performance, as a single rectangular region can contain two objects with different dynamics. Prinet et al (2006) and Xu et al (2008) also presented region-based approaches to optical flow. However, their segmentation assumptions cannot be applied to light microscopy images owing to the lack of edge and color information.…”

Section: Approachmentioning

confidence: 99%

Fast and robust optical flow for time-lapse microscopy using super-voxels

2012

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Motivation: 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.Results: We solve optical flow in large 3D time-lapse microscopy datasets by defining a Markov random field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D time-lapse datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is, on average, 10 × faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow end point error by 50% in regions with complex dynamic processes, such as cell divisions.Availability: Source code freely available in the Software section at http://janelia.org/lab/keller-lab.Contact: amatf@janelia.hhmi.org or kellerp@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.

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Section: Approachmentioning

confidence: 99%

Fast and robust optical flow for time-lapse microscopy using super-voxels

2012

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“…Xu et al [18] used segmentation based method to do the motion estimation. Prinet et al [11] proposed one method that can compute global and local motion of multi-structure objects. In [13,14], they model motion in a statistical way by learning the model with ground truth.…”

Section: Introductionmentioning

confidence: 99%