Editing Motion estimation (section)

==Algorithms==
The methods for finding motion vectors can be categorised into pixel based methods ("direct") and feature based methods ("indirect"). A famous debate resulted in two papers from the opposing factions being produced to try to establish a conclusion.<ref>Philip H.S. Torr and Andrew Zisserman: [https://www.robots.ox.ac.uk/~vgg/publications/2000/Torr00a/torr00a.pdf Feature Based Methods for Structure and Motion Estimation], ICCV Workshop on Vision Algorithms, pages 278-294, 1999</ref><ref>Michal Irani and P. Anandan: [https://web.archive.org/web/20180102072903/https://pdfs.semanticscholar.org/3d18/95f35202c2f421491df10105ff83c851ebd1.pdf About Direct Methods], ICCV Workshop on Vision Algorithms, pages 267-277, 1999.</ref>

===Direct methods===
* [[Block-matching algorithm]]
* [[Phase correlation]] and frequency domain methods
* Pixel recursive algorithms
* [[Optical flow]]

===Indirect methods===
''Indirect methods'' use features, such as [[corner detection]], and match corresponding features between frames, usually with a statistical function applied over a local or global area. The purpose of the statistical function is to remove matches that do not correspond to the actual motion.

Statistical functions that have been successfully used include [[RANSAC]].

===Additional note on the categorization===
It can be argued that almost all methods require some kind of definition of the matching criteria. The difference is only whether you summarise over a local image region first and then compare the summarisation (such as feature based methods),  or you compare each pixel first (such as squaring the difference) and then summarise over a local image region (block base motion and filter based motion). An emerging type of matching criteria summarises a local image region first for every pixel location (through some feature transform such as Laplacian transform), compares each summarised pixel and summarises over a local image region again.<ref>Rui Xu, David Taubman & Aous Thabit Naman, '[https://ieeexplore.ieee.org/abstract/document/7370941/ Motion Estimation Based on Mutual Information and Adaptive Multi-scale Thresholding]', in IEEE Transactions on Image Processing, vol. 25, no. 3, pp. 1095-1108, March 2016.</ref> Some matching criteria have the ability to exclude points that do not actually correspond to each other albeit producing a good matching score, others do not have this ability, but they are still matching criteria.