Challenges of deep unsupervised optical flow estimation for particle-image velocimetry data-Reference-Cited by-同舟云学术

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Challenges of deep unsupervised optical flow estimation for particle-image velocimetry data

Published:2024-02-19 Issue:3 Volume:65 Page:
ISSN:0723-4864
Container-title:Experiments in Fluids
language:en
Short-container-title:Exp Fluids

Author:

Lagemann Christian,Lagemann Kai,Mukherjee Sach,Schröder Wolfgang

Funder

Deutsche Forschungsgemeinschaft

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00348-024-03768-2.pdf

Reference41 articles.

1. Astarita T, Cardone G (2005) Analysis of interpolation schemes for image deformation methods in PIV. Exp Fluids 38(2):233–243

2. Barron JT (2019) A general and adaptive robust loss function. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4331–4339

3. Cai S, Zhou S, Xu C, Gao Q (2019) Dense motion estimation of particle images via a convolutional neural network. Exp Fluids 60(4):73

4. Charbonnier P, Blanc-Feraud L, Aubert G, Barlaud M (1994) Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of 1st international conference on image processing, IEEE, vol. 2, pp 168–172

5. Corpetti T, Heitz D, Arroyo G, Mémin E, Santa-Cruz A (2006) Fluid experimental flow estimation based on an optical-flow scheme. Exp Fluids 40(1):80–97

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