65,486 research outputs found
Simultaneous Image Registration and Monocular Volumetric Reconstruction of a fluid flow
We propose to combine image registration and volumetric reconstruction from a monocular video of a draining off Hele-Shaw cell filled with water. A Hele-Shaw cell is a tank whose depth is small (e.g. 1 mm) compared to the other dimensions (e.g. 400 800 mm2). We use a technique known as molecular tagging which consists in marking by photobleaching a pattern in the fluid and then tracking its deformations. The evolution of the pattern is filmed with a camera whose principal axis coincides with the depth of the cell. The velocity of the fluid along this direction is not constant. Consequently,tracking the pattern cannot be achieved with classical methods because what is observed is the integration of the marked particles over the entire depth of the cell. The proposed approach is built on top of classical direct image registration in which we incorporate a volumetric image formation model. It allows us to accurately measure the motion and the velocity profiles for the entire volume (including the depth of the cell) which is something usually hard to achieve. The results we obtain are consistent with the theoretical hydrodynamic behaviour for this flow which is known as the laminar Poiseuille flow
Image registration algorithm for molecular tagging velocimetry applied to unsteady flow in Hele-Shaw cell
In order to develop velocimetry methods for confined geometries, we propose to combine image registration and volumetric reconstruction from a monocular video of the draining of a Hele-Shaw cell filled with water. The cell’s thickness is small compared to the other two dimensions (e.g. 1x400 x 800 mm3). We use a technique known as molecular tagging which consists in marking by photobleaching a pattern in the fluid and then tracking its deformations. The evolution of the pattern is filmed with a camera whose principal axis coincides with the cell’s gap. The velocity of the fluid along this direction is not constant. Consequently, tracking the pattern cannot be achieved with classical methods because what is observed is the integral of the marked molecules over the entire cell’s gap. The proposed approach is built on top of direct image registration that we extend to specifically model the volumetric image formation. It allows us to accurately measure the motion and the velocity profiles for the entire volume (including the cell’s gap) which is something usually hard to achieve. The results we obtained are consistent with the theoretical hydrodynamic behaviour for this flow which is known as the Poiseuille flow
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
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