spacer.png, 0 kB
  spacer.png, 0 kB
spacer.png, 0 kB
spacer.png, 0 kB
Optical Flow For Live Cell Imaging PDF Print
Article Index
Optical Flow For Live Cell Imaging
Optical Flow Methods
Ground Truth Image Series Generator
Motion Tracking Application
Related Publications

Ground Truth Image Series Generator

For the sake of performance analysis of any optical flow computation method on particular image data, we had developed the gtgen tool --- a ground-truth generator.

It allows for automatic generation of high-fidelity image sequences based on real-world sample image as well as corresponding artificial flow field sequences [3] [5] . We've accented the fidelity to the sample image in our approach since the performance of given optical flow computation method may substantially differ on different sorts of images. Hence, the analysis on given data doesn't have to be valid for another sort of images.

The goal was to design a fully-automatic easy-to-use solution so a large dataset of image sequences, resembling real application in mind, accompanied with correct results, what we call ground-truth, can be made available quickly. We refer to ground-truth flow fields as to created 2D or 3D flow fields that describe the movements displayed in the generated images.

Our tool benefits of a two-layered approach in which user-selected foreground is locally moved and inserted into an artificially generated background. The background is visually similar to the sample real image while the foreground is extracted from it and so its fidelity is guaranteed.

The tool requires following inputs:

  • sample image, the output must look like this image
  • mask image of background, this determines the background region where global motion occurs
  • mask image of foreground, this determines the foreground regions where additional local motions occur
  • mask image of possible positions, this determines regions in which foreground components are allowed to appear
  • definition of global motion which remains constant throughout the entire generated sequence
  • number of frames of the sequence to be created

Sample image
Sample image
Background mask
Background mask

Foreground mask
Foreground mask

Mask of possible positions
Mask of possible positions


Foreground motion is suggested for each foreground component for each frame individually and automaticaly by the gtgen tool. The determination of a new position considers the previous movement of this component as well as the mask of possible positions in which the component must remain.

Path visualization
Visualization of positions of foreground components in green within the mask in blue. There was no global motion present. Hence, only local movements are visible. The brightest intensity shows the first position.

Video sequence
Video sequence of this example.

The gtgen is capable of generating 2D and 3D image sequences of arbitrary length [2] .

Example 1, results
Example of possible positions mask in the left. For this mask, an image of all positions where foreground components occured during the time-lapse sequence in the right.
Example 1, video
Video sequence of this example where no global movements were performed.

Example 2, video
Video sequence of the same example with global movement.

Example 3, results
Example of the mask of possible positions. Notice shapes and how they control local movements in the following video.
Example 3, video
Video sequence in which this mask of possible positions was applied.

Example 4, video
Video of an example of generated 3D time-lapse sequence. Notice that the generator can handle overlapping of foreground components.


The ground-truth generator is available under GNU GPL as part of the OpticalFlow collection as a tool gtgen

The gtgen-ng tool

We are currently developing a new generation [5] of the time-lapse ground-truth datasets generator, which employs GUI control interface and plug-ins based scheme to allow for simulation of general motion and events to be observed in the generated time-lapse image sequence. The CBIA CytoPacq is used for generation of synthetic and highly realistic image content.

Example of synthetically generated time-lapse sequence can be downloaded here. Only the image and ground-truth binary mask of the observed cell nuclei are displayed in the file. The method, however, produced ground-truth optical flow fields as well.

Written by Vladimír Ulman   
Last Updated ( Wednesday, 19 September 2012 )
spacer.png, 0 kB
spacer.png, 0 kB
spacer.png, 0 kB