Speckle tracking approaches in speckle correlation sensing

Data and code used to generate the conference paper:

"Speckle tracking approaches in speckle correlation sensing"

Thomas O. H. Charrett, Krzysztof Kotowski, and Ralph P. Tatam

SPIE Optics and Optoelectonics, Prague, 2017.

Files:

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lib_feature_tracking.py - python module/library used to simplify the other scripts

feature detectors.py - python script used to test processing times of feature detectors.

feature descriptors.py - python script used to test processing times of feature descriptors and matching methods

modelled shifts.py - python script used to generate figure 1 - accuracy assesment.

experimental shifts.py - python script used to compare feature tracking method with cross correlation using real data (figure 2)

experimental rotations.py - python script used to test rotation performance using experimental data. Used to generate figure 3.

random positions.npy - 100 x (512,512) independent speckle patterns in numpy binary format. Used for table 1, table 2 and figure 1

linear move direction=0.0 speed=5.0mms-1.npy - 100 x (512,512) speckle patterns recorded using a speckle velocimetry sensor on XY stages travelling at 5mm/s in the y-direction. In numpy binary format.Used for figure 2.

z rotation.npy - 721 x (512,512) speckle patterns for angles 0 to 360.0 degrees in 0.5 degree steps. Used for figure 3.

Comments:

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OpenCV version: 3.1.0

Numpy python library available at http://www.numpy.org/.

Numpy version: 1.10.2

Load numpy binary format using:

>>> import numpy as np

>>> imgs = np.load( filename )