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VTTrac

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Documentation for VTTrac.

VTTrac: Velocimetry by Template Tracking

This library provides the julia-language implementation for VTTrac (https://github.com/thorinouchi/VTTrac). It does not use module variables, so it should be good for parallel execution.

The algorithm used in this library is the simple template matching of PIV (particle image velocimetry) for monochromatic image-like data, but the matching is conducted multiple times in a Lagrangian manner as in PTV (particle tracking velocimetry) over a number of times specified by the parameter named ntrac. The default scoring method for template matching is the cross correlation coefficient, as in the basic PIV. Both forward and backward tracking is available. Use the parameter itstep; tracking is backward along time sequence, if it is negative.

Available scoring methods

  • XCOR: cross-correlation, cov(x',y')/sig(x)/sig(y), where x represents the template sub-image and y represents the target sub-image that are slided.
  • NCOV: normalized covariance, cov(x',y')/sig(x)^2: covariance normalized the variance of the template sub-image x.

Screening

A check (result screening) based on velocity change along trajectory is available (by using the threshold parameter named vxch and vych), so it is recommended to always set ntrac >= 2. Further screening is available for initial templates (e.g., in terms of the complexity and contrast) and the quality of the results (e.g., score threshold); see the source code.

Dimensions

Spatial coordinates are based on array indices, with the distance between adjacent grid points always being 1, so they are non-dimensional. The velocities are based on non-dimensional spacial displacement over time difference, where, time can either be dimensional or non-dimensional.

References

  • VTTrac by Takeshi Horinouchi: https://github.com/thorinouchi/VTTrac
  • pyVTTrac by Taiga Tsukada: https://github.com/tsukada-cs/pyVTTrac