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== Estimation ==
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>
Fleet, along with John L. Barron
The optical flow methods try to calculate the motion between two image frames which are taken at times <math>t</math> and <math>t+\Delta t</math> at every [[voxel]] position. These methods are called differential since they are based on local [[Taylor series]] approximations of the image signal; that is, they use partial derivatives with respect to the spatial and temporal coordinates.
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