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== Estimation ==
Optical flow can be estimated in a number of ways. Broadly, optical flow estimation approaches can be divided into machine learning based models (sometimes called data-driven models), classical models (sometimes called knowledge-driven models) which do not use machine learning and hybrid models which use aspects of both learning based models and classical models.<ref name="Zhai_Survey_2021">{{cite journal |last1=Zhai |first1=Mingliang |last2=Xiang |first2=Xuezhi |last3=Lv |first3=Ning |last4=Kong |first4=Xiangdong |title=Optical flow and scene flow estimation: A survey |journal=Pattern Recognition |date=2021 |volume=114 |pages=107861 |doi=https://doi.org/10.1016/j.patcog.2021.107861 |url=www.sciencedirect.com}}</ref>
===Classical Models===
===Learning Based Models===
Sequences of ordered images allow the estimation of motion as either instantaneous image velocities or discrete image displacements.<ref name="S. S. Beauchemin, J. L. Barron 1995" /> David J. Fleet and Yair Weiss provide a tutorial introduction to gradient based optical flow.<ref>{{Cite book |title=Handbook of Mathematical Models in Computer Vision |last1=Fleet |first1=David J. |last2=Weiss |first2=Yair |publisher=Springer |year=2006 |isbn=978-0-387-26371-7 |editor-last=Paragios |editor-first=Nikos |pages=237–257 |chapter=Optical Flow Estimation |editor-last2=Chen |editor-first2=Yunmei |editor-last3=Faugeras |editor-first3=Olivier D. |chapter-url=http://www.cs.toronto.edu/~fleet/research/Papers/flowChapter05.pdf}}</ref>
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