Speckle tracking approaches in speckle correlation sensing
datasetposted on 02.05.2017 by Tom Charrett, Krzysztof Kotowski, Ralph Tatam
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.
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.
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 )