Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging
The source for the ring detection algorithm described in Sci. Rep. 5, 13584; doi: 10.1038/srep13584 (2015).
Some usage examples can be found in the accompanying ipython-notebook code/demo.ipynb; a static view is available here;
An easy way to get the required dependencies is using Anaconda, which is cross-platform, or any other Python distribution which includes Cython, Scipy and OpenCV.
Acknowledgements would be highly appreciated; for academic citation please use:
Afik, E. Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging. Sci. Rep. 5, 13584; doi: 10.1038/srep13584 (2015).
- E. Afik, Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging. Sci. Rep. 5, 13584; doi: 10.1038/srep13584 (2015)
- E. Afik and V. Steinberg. Pair dispersion in a chaotic flow reveals the role of the memory of initial velocity. ArXiv e-prints arXiv:1502.02818. submitted.
- particle-tracking -- A linking algorithm for particle tracking in n-dimensions, implementing a kinematic model and a memory feature to account for occasional misses.
- natural-cubic-smoothing-splines -- a natural cubic smoothing splines module to smooth-out noise and obtain an estimate of the first two derivatives, i.e. velocity and acceleration in the case of a particle trajectory.