Speckle tracking approaches in speckle correlation sensing
| dc.contributor.author | Charrett, Tom | |
| dc.contributor.author | Kotowski, Krzysztof | |
| dc.contributor.author | Tatam, Ralph | |
| dc.date.accessioned | 2024-06-11T09:36:44Z | |
| dc.date.available | 2024-06-11T09:36:44Z | |
| dc.date.issued | 2017-05-02 13:08 | |
| dc.description.abstract | 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 ) | |
| dc.description.sponsorship | EPSRC EP/M020401/1, EP/N002520/1 | |
| dc.identifier.citation | Charrett, Tom; Kotowski, Krzysztof; Tatam, Ralph (2017). Speckle tracking approaches in speckle correlation sensing. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.4785721 | |
| dc.identifier.doi | 10.17862/cranfield.rd.4785721 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/22379 | |
| dc.publisher | Cranfield University | |
| dc.relation.isreferencedby | https://doi.org/10.1117/12.2264225' | |
| dc.rights | CC BY 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | laser speckle' | |
| dc.subject | 'image processing' | |
| dc.subject | 'feature tracking' | |
| dc.subject | 'Photonics and Electro-Optical Engineering (excl. Communications)' | |
| dc.subject | 'Signal Processing' | |
| dc.title | Speckle tracking approaches in speckle correlation sensing | |
| dc.type | Dataset |
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