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Other possible local loss functions include the negative normalized [[cross-correlation]] between the two frames.<ref>{{cite conference |last=Lucas |first=Bruce D. |last2=Kanade |first2=Takeo |date=1981-08-24 |title=An iterative image registration technique with an application to stereo vision |url=https://dl.acm.org/doi/10.5555/1623264.1623280 |journal=Proceedings of the 7th International Joint Conference on Artificial intelligence - Volume 2 |series=IJCAI'81 |___location=San Francisco, CA, USA |publisher=Morgan Kaufmann Publishers Inc. |pages=674–679}}</ref>
===Learning
Instead of seeking to model optical flow directly, one can train a [[machine learning]] system to estimate optical flow. Since 2015, when FlowNet<ref>{{Cite conference |last=Dosovitskiy |first=Alexey |last2=Fischer |first2=Philipp |last3=Ilg |first3=Eddy |last4=Hausser |first4=Philip |last5=Hazirbas |first5=Caner |last6=Golkov |first6=Vladimir |last7=Smagt |first7=Patrick van der |last8=Cremers |first8=Daniel |last9=Brox |first9=Thomas |date=2015 |title=FlowNet: Learning Optical Flow with Convolutional Networks |url=https://ieeexplore.ieee.org/document/7410673/ |publisher=IEEE |pages=2758–2766 |doi=10.1109/ICCV.2015.316 |isbn=978-1-4673-8391-2 | conference=2015 IEEE International Conference on Computer Vision (ICCV)}}</ref> was proposed, learning based models have been applied to optical flow and have gained prominence. Initially, these approaches were based on [[Convolutional neural network|Convolutional Neural Networks]] arranged in a [[U-Net]] architecture. However, with the advent of [[Transformer (deep learning architecture)|transformer architecture]] in 2017, transformer based models have gained prominence.<ref>{{Cite journal |last=Alfarano |first=Andrea |last2=Maiano |first2=Luca |last3=Papa |first3=Lorenzo |last4=Amerini |first4=Irene |date=2024 |title=Estimating optical flow: A comprehensive review of the state of the art |url=https://linkinghub.elsevier.com/retrieve/pii/S1077314224002418 |journal=Computer Vision and Image Understanding |language=en |volume=249 |pages=104160 |doi=10.1016/j.cviu.2024.104160}}</ref>
Most learning-based approaches to optical flow use [[supervised learning]]. In this case, many frame pairs of video data and their corresponding [[ground truth|ground-truth]] flow fields are used to optimise the parameters of the learning-based model to accurately estimate optical flow. This process often relies on vast training datasets due to the number of parameters involved.<ref name="Tu_2019_Survey">{{cite journal |last1=Tu |first1=Zhigang |last2=Xie |first2=Wei |last3=Zhang |first3=Dejun |last4=Poppe |first4=Ronald |last5=Veltkamp |first5=Remco C. |last6=Li |first6=Baoxin |last7=Yuan |first7=Junsong |title=A survey of variational and CNN-based optical flow techniques |journal=Signal Processing: Image Communication |date=1 March 2019 |volume=72 |pages=9–24 |doi=10.1016/j.image.2018.12.002}}</ref>
== Uses ==
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