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:
------
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:
---------
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 )