- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Advances in Neural Information Processing Systems 25 (NIPS 2012). https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf Cited May 11, 2023.
- J. Rienitz, “Schlieren Experiment 300 Years Ago”, Nature 254 (5498), 293-295 (1975). DOI: 10.1038/254293a0
- G. S. Settles and M. J. Hargather, “A Review of Recent Developments in Schlieren and Shadowgraph Techniques”, Meas. Sci. Technol. 28 (4), Article Number 042001 (2017). DOI: 10.1088/1361-6501/aa5748
- J. Wolfram and J. Martinez Schramm, “Pattern Recognition in High Speed Schlieren Visualization at the High Enthalpy Shock Tunnel Göttingen (HEG)”, in Notes on Numerical Fluid Mechanics and Multidisciplinary Design (Springer, Berlin, 2010), Vol. 112, pp. 399-406. DOI: 10.1007/978-3-642-14243-7_49
- N. T. Smith, M. J. Lewis, and R. Chellappa, “Extraction of Oblique Structures in Noisy Schlieren Sequences Using Computer Vision Techniques”, AIAA J. 50 (5), 1145-1155 (2012). DOI: 10.2514/1.J051335
- C. Liu, R. Jiang, D. Wei, et al., “Deep Learning Approaches in Flow Visualization”, Adv. Aerodyn. 4, Article Number 17 (2022). DOI: 10.1186/s42774-022-00113-1
- Y. Liu, Y. Lu, Y. Wang, et al., “A CNN-Based Shock Detection Method in Flow Visualization”, Comput. Fluids 184, 1-9 (2019). DOI: 10.1016/j.compfluid.2019.03.022
- S. Cui, Y. Wang, X. Qian, and Z. Deng, “Image Processing Techniques in Shockwave Detection and Modeling”, J. Signal Inform. Process. 4 (3B), 109-113 (2013). DOI: 10.4236/jsip.2013.43B019
- G. Li, M. Burak Agir, K. Kontis, et al., “Image Processing Techniques for Shock Wave Detection and Tracking in High Speed Schlieren and Shadowgraph Systems”, J. Phys.: Conf. Ser. 1215, Article Number 012021 (2019). DOI: 10.1088/1742-6596/1215/1/012021
-
N. T. Smith, M. J. Lewis, and R. Chellappa, "Detection, Localization, and Tracking of Shock Contour Salient Points in Schlieren Sequences", AIAA J. 52 (6), 1249-1264 (2014). DOI: 10.2514/1.J052367
-
T. R. Fujimoto, T. Kawasaki, and K. Kitamura, "Canny-Edge-Detection/Rankine-Hugoniot-Conditions Unified Shock Sensor for Inviscid and Viscous Flows", J. Comput. Phys. 396, 264-279 (2019). DOI: 10.1016/j.jcp.2019.06.071 EDN: SSVACB
-
M. V. Srisha Rao and G. Jagadeesh, "Visualization and Image Processing of Compressible Flow in a Supersonic Gaseous Ejector", J. Indian Inst. Sci. 93 (1), 57-66 (2013).
-
P. V. Bulat, K. N. Volkov, and M. S. Yakovchuk, "Flow Visualization with Strong and Weak Gas Dynamic Discontinuities in Computational Fluid Dynamics", Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie). 17 (3), 245-257 (2016). DOI: 10.26089/NumMet.v17r323 EDN: VRHROJ
-
P. V. Bulat and K. N. Volkov, "Visualization of Gas Dynamics Discontinuities in Supersonic Flows Using Digital Image Processing Methods", Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie). 20 (3), 237-253 (2019). DOI: 10.26089/NumMet.v20r322 EDN: RNGYBE
-
C. Brossard, J. C. Monnier, P. Barricau, et al., "Principles and Applications of Particle Image Velocimetry", Aerospace Lab. No. 1, 1-11 (2009). https://hal.science/hal-01180587 Cited May 6, 2023.
-
E. K. Akhmetbekov, A. V. Bilsky, Yu. A. Lozhkin, et al., "Software for Experiment Management and Processing of Data Obtained by Digital Flow Visualization Techniques (ActualFlow)", Numerical Methods and Programming. (Vychislitel'nye Metody i Programmirovanie). 7 (3), 79-85 (2006). EDN: IBLLAX
-
E. Arnaud, E. Mémin, R. Sosa, and G. Artana, "A Fluid Motion Estimator for Schlieren Image Velocimetry", in Lecture Notes in Computer Science (Springer, Berlin, 2006), Vol. 3951, pp. 198-210. DOI: 10.1007/11744023_16
-
M. Lawson, M. Hargather, G. Settles, et al., "Focusing-Schlieren PIV Measurements of a Supersonic Turbulent Boundary Layers", in Proc. 47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition, Orlando, USA, January 5-8, 2009. https://arc.aiaa.org/doi/abs/10.2514/6.2009-69 Cited May 5, 2023. DOI: 10.2514/6.2009-69CitedMay5
-
A. W. Gena, C. Voelker, and G. S. Settles, "Qualitative and Quantitative Schlieren Optical Measurement of the Human Thermal Plume", Indoor Air 30 (4), 757-766 (2020). DOI: 10.1111/ina.12674
-
M. J. Hargather, M. J. Lawson, G. S. Settles, and L. M. Weinstein, "Seedless Velocimetry Measurements by Schlieren Image Velocimetry", AIAA J. 49 (3), 611-620 (2011). DOI: 10.2514/1.J050753 EDN: OELXEX
-
M. Debella-Gilo and A. Kääb, "Sub-Pixel Precision Image Matching for Measuring Surface Displacements on Mass Movements Using Normalized Cross-Correlation", Remote Sens. Environ. 115 (1), 130-142 (2011). DOI: 10.1016/j.rse.2010.08.012
-
M. Berenjkoub, G. Chen, and T. Günther, "Vortex Boundary Identification Using Convolutional Neural Network", in Proc. 2020 IEEE Visualization Conference (VIS), Salt Lake City, USA, October 25-30, 2020 (IEEE Press, New York, 2020), pp. 261-265. DOI: 10.1109/VIS47514.2020.00059
-
A. D. Beck, J. Zeifang, A. Schwarz, and D. G. Flad, "A Neural Network Based Shock Detection and Localization Approach for Discontinuous Galerkin Methods", J. Comput. Phys. 423, Article Number 109824 (2020). DOI: 10.1016/j.jcp.2020.109824
-
M. Morimoto, K. Fukami, and K. Fukagata, "Experimental Velocity Data Estimation for Imperfect Particle Images Using Machine Learning", Phys. Fluids. 33 (8), Article Number 087121 (2021). DOI: 10.1063/5.0060760
-
B. N. Ubald, P. Seshadri, and A. Duncan, "Density Reconstruction from Schlieren Images through Bayesian Nonparametric Models", arXiv preprint: 2201.05233v3 [physics.flu-dyn] (Cornell Univ. Library, Ithaca, 2022). DOI: 10.48550/arXiv.2201.05233
-
M. Monfort, T. Luciani, J. Komperda, et al., "A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields", in Modeling, Analysis, and Visualization of Anisotropy (Springer, Cham, 2017), pp. 375-392. DOI: 10.1007/978-3-319-61358-1_16
-
G. B'iró, M. Pocsai, I. F. Barna, et al., "Machine Learning Methods for Schlieren Imaging of a Plasma Channel in Tenuous Atomic Vapor", Opt. Laser Technol. 159, Article Number 108948 (2023). DOI: 10.1016/j.optlastec.2022.108948
-
B. Colvert, M. Alsalman, and E. Kanso, "Classifying Vortex Wakes Using Neural Networks", Bioinspir. Biomim. 13 (2), Article Number 025003 (2018). DOI: 10.1088/1748-3190/aaa787
-
M. D. Manshadi, H. Vahdat-Nejad, M. Kazemi-Esfeh, and M. Alavi, "Speed Detection in Wind-Tunnels by Processing Schlieren Images", Int. J. Eng. 29 (7), 962-967 (2016). DOI: 10.5829/idosi.ije.2016.29.07a.11
-
Shadowgraph Images. Datasets at Hugging Face. https://huggingface.co/datasets/igor3357/shadowgraph_images Cited May 6, 2023.
-
Ultralytics YOLOv8. https://github.com/ultralytics/ultralytics Cited May 6, 2023.
-
I. A. Znamenskaya and I. A. Doroshchenko, "Edge Detection and Machine Learning for Automatic Flow Structures Detection and Tracking on Schlieren and Shadowgraph Images", J. Flow Vis. Image Process. 28 (4), 1-26, (2021). DOI: 10.1615/JFlowVisImageProc.2021037690
-
I. A. Znamenskaya, I. A. Doroshchenko, N. N. Sysoev, and D. I. Tatarenkova, "Results of Quantitative Analysis of High-Speed Shadowgraphy of Shock Tube Flows Using Machine Vision and Machine Learning", Dokl. Akad. Nauk 497 (1), 16-20 (2021) [Dokl. Phys. 66 (4), 93-96, (2021)]. DOI: 10.1134/S1028335821040066 EDN: WYGCBP