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: ------ 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 )