Main Features

Main Features:
Particle Tracking Algorithms:
* Particles detection based on Binary correlation, Gaussian mask and Dynamic Threshold Binarization techniques.
* Cross-correlation and Relaxation algorithms for solution of temporal matching problem.
* Particle labelling and tracking of individual trajectories.
Pre and Post-processing:
* Extraction of image background to improve SNR.
* Multiple vector validation methods.
* Data smoothing
* Extraction of time series.
* Interpolation of the results on a regular mesh for standard Eulerian analysis.
* Calculation of streamlines
Graphical User Interface (adapted from PIVlab):
* Extensive data extraction tools.
* Synthetic PIV/PTV image generator.
* Several data export features.
* Import bmp/ tiff/ jpeg image pairs/ series
* Individual image masking and region of interest.
* Multiple colormaps.

Friday, 1 March 2013

PTVlab Beta released

PTVlab (Particle Tracking Velocimetry - lab) is a Matlab software featuring state of the art mathematical algorithms and a Graphical User Interface (GUI) adapted from the open source project PIVlab. 

This software aims at the analysis of experimental Image Velocimetry measurements using a Lagrangian frame of reference, which can offer several benefits compared to the standard Particle Image Velocimetry (PIV) technique. Several institutions have been involved in the development of PTVlab. Dr Wernher Brevis mainly developed the underlying mathematical algorithms and their implementation during his PhD studies at the University of Chile, Chile and Karlsruhe Institute of Technology, Germany (Brevis et al, 2011). Dr Brevis’s research groups at the University of Sheffield, United Kingdom, have developed new algorithms and bug fix. The adaptation of the graphical user interface of PIVlab and the development of new functionalities have been contributed by Antoine Patalano, as part of his PhD studies at the National University of Cordoba, Argentina. Our intention is to contribute with an open source, state of the art and easy to use tool for the analysis of experimental fluid mechanics images, thus we are releasing a Beta version of the software for testing. There are still several bugs that need attention but the main functionalities have been tested and validated.

Feel free to ask any questions regarding the implemented functionalities. Any feedback, including improvement, the request of new features and bugs report are especially important to keep improving the project.

This project would like to thank Dipl. Biol. William Thielicke and Prof. Dr. Eize J. Stamhuis for sharing the codes and GUI of PIVlab.


Brevis W, Niño Y and Jirka GH (2011) Integrating cross-correlation and relaxation algorithms for particle tracking velocimetry. Experiments in Fluids, 50 (1), pp 135-147. ISSN 0723-4864. DOI 10.1007/s00348-010-0907-z

Antoine Patalano is a member of the Center for Water Studies and Technology (CETA) of the National University of Córdoba in Argentina.


In the video below the particle density is high for “standard” PTV analysis. The results show the performance of the algorithms, which are able to detect individual particles and calculate their associated velocity.


  1. Thanks Antoine for your contribution!

  2. I wanted to thank you for this great read!! I definitely enjoying every little bit of it I have you bookmarked to check out new stuff you post.
    Defect Bug Tracking

  3. Thanks for sharing this with us! Some really amazing features.

    time tracker

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