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*Block-based methods – minimizing sum of squared differences or [[sum of absolute differences]], or maximizing normalized [[cross-correlation]]
*Differential methods of estimating optical flow, based on partial derivatives of the image signal and/or the sought flow field and higher-order partial derivatives, such as:
**[[Lucas–Kanade method]] – regarding image patches and an affine model for the flow field<ref name="Zhang2018">{{Cite journal |last1=Zhang |first1=G. |last2=Chanson |first2=H. |author-link2=Hubert Chanson |year=2018 |title=Application of Local Optical Flow Methods to High-Velocity Free-surface Flows: Validation and Application to Stepped Chutes |url=http://staff.civil.uq.edu.au/h.chanson/reprints/Zhang_Chanson_etfs_2018.pdf |journal=Experimental Thermal and Fluid Science |volume=90 |pages=186–199 |doi=10.1016/j.expthermflusci.2017.09.010|bibcode=2018ETFS...90..186Z }}</ref>
**[[Horn–Schunck method]] – optimizing a functional based on residuals from the brightness constancy constraint, and a particular regularization term expressing the expected smoothness of the flow field<ref name="Zhang2018" />
**[[Buxton–Buxton method]] – based on a model of the motion of edges in image sequences<ref>{{Cite book |url=https://books.google.com/books?id=NiQXkMbx-lUC&q=optical-flow+Buxton-and-Buxton&pg=PA107 |title=Visual Cognition |last=Glyn W. Humphreys and [[Vicki Bruce]] |publisher=Psychology Press |year=1989 |isbn=978-0-86377-124-8}}</ref>
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