A Fast Shape-from-Focus-Based Surface Topography Measurement Method
Abstract
:1. Introduction
2. Methodology
2.1. Traditional Shape from Focus
2.2. Two-Step Shape from Focus
Process Parameters
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
3D | Three Dimensional |
DFF | Depth From Focus |
DOF | Depth Of Field |
ETL | Electronically Tunable Lens |
FMO | Focus Measure Operator |
FOV | Field Of View |
GLVM | Modified Gray Level Variance |
GPU | Graphical Processing Unit |
LT | Laser Triangulation |
ICP | Iterative Closest Points |
MDPI | Multidisciplinary Digital Publishing Institute |
ICP | Iterative Closest Points |
PTC | Portable Calibration Target |
SFF | Shape From Focus |
STL | Stereo Lithography |
References
- Newton, L.; Senin, N.; Gomez, C.; Danzl, R.; Helmli, F.; Blunt, L.; Leach, R. Areal topography measurement of metal additive surfaces using focus variation microscopy. Addit. Manuf. 2019, 25, 365–389. [Google Scholar] [CrossRef]
- Angot-Petit, L.J. Small scale surface profile recovery using a tunable lens based System. Electron. Imaging 2017, 39–48. [Google Scholar] [CrossRef]
- Flack, D.; Hannaford, J. Fundamental Good Practice in Dimensional Metrology; National Physical Laboratory: Teddington, UK, 2009; p. 227. [Google Scholar]
- Kienle, P.; Batarilo, L.; Akgül, M.; Köhler, M.H.; Wang, K.; Jakobi, M.; Koch, A.W. Optical Setup for Error Compensation in a Laser Triangulation System. Sensors 2020, 20, 4949. [Google Scholar] [CrossRef] [PubMed]
- Genta, G.; Minetola, P.; Barbato, G. Calibration procedure for a laser triangulation scanner with uncertainty evaluation. Opt. Lasers Eng. 2016, 86, 11–19. [Google Scholar] [CrossRef]
- Boehler, W.; Marbs, A. Investigating Laser Scanner Accuracy. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2003, 34, 696–701. [Google Scholar] [CrossRef]
- Chen, Z.; Liao, H.; Zhang, X. Telecentric stereo micro-vision system: Calibration method and experiments. Opt. Lasers Eng. 2014, 57, 82–92. [Google Scholar] [CrossRef]
- Calì, M.; Ambu, R. Advanced 3D photogrammetric surface reconstruction of extensive objects by UAV camera image acquisition. Sensors 2018, 18, 2815. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Brosed, F.J.; Aguilar, J.J.; Santolaria, J.; Lázaro, R. Geometrical Verification based on a Laser Triangulation System in Industrial Environment. Effect of the Image Noise in the Measurement Results. In Procedia Engineering; Elsevier Ltd.: Amsterdam, The Netherlands, 2015; Volume 132, pp. 764–771. [Google Scholar] [CrossRef] [Green Version]
- Swojak, N.; Wieczorowski, M.; Jakubowicz, M. Assessment of selected metrological properties of laser triangulation sensors. Meas. J. Int. Meas. Confed. 2021, 176. [Google Scholar] [CrossRef]
- Hu, Y.; Chen, Q.; Feng, S.; Zuo, C. Microscopic fringe projection profilometry: A review. Opt. Lasers Eng. 2020, 135, 106192. [Google Scholar] [CrossRef]
- Pentland, A.P. A New Sense for Depth of Field. IEEE Trans. Pattern Anal. Mach. Intell. 1987, PAMI-9, 523–531. [Google Scholar] [CrossRef] [PubMed]
- Moeller, M.; Benning, M.; Schönlieb, C.; Cremers, D. Variational Depth from Focus Reconstruction. IEEE Trans. Image Process. 2015, 24, 5369–5378. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mahmood, F.; Mahmood, M.T.; Iqbal, J. 3-D shape recovery from image focus using no-reference sharpness metric based on inherent sharpness. In Proceedings of the International Conference on Control, Automation and Systems, Jeju, Korea, 18–21 October 2017. [Google Scholar] [CrossRef]
- Optotune. Fast Electrically Tunable Lens EL-10-30 Series. 2019. Available online: https://www.optotune.com/s/Optotune-EL-10-30.pdf (accessed on 19 September 2019).
- Pertuz, S.; Puig, D.; Garcia, M.A. Analysis of focus measure operators for shape-from-focus. Pattern Recognit. 2013, 46, 1415–1432. [Google Scholar] [CrossRef]
- Hazirbas, C.; Soyer, S.G.; Staab, M.C.; Leal-Taixé, L.; Cremers, D. Deep Depth from Focus. In Proceedings of the Asian Conference on Computer Vision (ACCV 2018), Perth, WA, Australia, 2–6 December 2018; pp. 525–541. [Google Scholar] [CrossRef]
- Fialka, O.; Čadík, M. FFT and convolution performance in image filtering on GPU. In Proceedings of the 10th International Conference on Information Visualisation (IV 2006), London, UK, 5–7 July 2006; pp. 609–614. [Google Scholar] [CrossRef]
- Chen, X.; Qiu, Y.; Yi, H. Implementation and performance of image filtering on GPU. In Proceedings of the 2013 International Conference on Intelligent Control and Information Processing (ICICIP 2013), Beijing, China, 9–11 June 2013; pp. 514–517. [Google Scholar] [CrossRef]
- Pertuz, S. Shape from Focus. 2019. Available online: https://nl.mathworks.com/matlabcentral/fileexchange/55103-shape-from-focus (accessed on 24 July 2019).
- Movimed. What Is Laser Triangulation? 2020. Available online: https://www.movimed.com/knowledgebase/what-is-laser-triangulation/ (accessed on 7 August 2020).
- Sun, B.; Li, B. A rapid method to achieve aero-engine blade form detection. Sensors 2015, 15, 12782–12801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Girardeau-Montaut, D.; Roux, M.; Marc, R.; Thibault, G. Change detection on points cloud data acquired with a ground laser scanner. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2005, 36, 30–35. [Google Scholar]
- Rajendra, Y.D.; Mehrotra, S.C.; Kale, K.V.; Manza, R.R.; Dhumal, R.K.; Nagne, A.D.; Vibhute, D.; Ramanujan, S.; Chair, G. Evaluation of Partially Overlapping 3D Point Cloud’s Registration by Using ICP Variant and Cloudcompare. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2014, 891–897. [Google Scholar] [CrossRef] [Green Version]
Measurement | Total Number of Images | Imaging Time (s) | Processing Time on CPU (s) | Total Measurement Time (s) |
---|---|---|---|---|
Conventional Shape from focus method | 25,350 | 507 | 844 | 1350 |
Two-step Shape from focus method | 14,411 | 288 | 436 | 724 |
Measurement | Mean Deviation from Reference (mm) | Standard Deviation (mm) |
---|---|---|
Conventional Shape from focus | 0.033 | |
Proposed Two-step approach | 0.026 | |
Laser Triangulation Measurement | 0.120 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gladines, J.; Sels, S.; Blom, J.; Vanlanduit, S. A Fast Shape-from-Focus-Based Surface Topography Measurement Method. Sensors 2021, 21, 2574. https://doi.org/10.3390/s21082574
Gladines J, Sels S, Blom J, Vanlanduit S. A Fast Shape-from-Focus-Based Surface Topography Measurement Method. Sensors. 2021; 21(8):2574. https://doi.org/10.3390/s21082574
Chicago/Turabian StyleGladines, Jona, Seppe Sels, Johan Blom, and Steve Vanlanduit. 2021. "A Fast Shape-from-Focus-Based Surface Topography Measurement Method" Sensors 21, no. 8: 2574. https://doi.org/10.3390/s21082574
APA StyleGladines, J., Sels, S., Blom, J., & Vanlanduit, S. (2021). A Fast Shape-from-Focus-Based Surface Topography Measurement Method. Sensors, 21(8), 2574. https://doi.org/10.3390/s21082574