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{{abbreviations|date=April 2012}}
'''Features from Accelerated Segment Test (FAST)''' is a corner detection method, which could be used to extract feature points and later used to track and map objects in many [[computer vision]] tasks. FAST corner detector was originally developed by Edward Rosten and Tom Drummond. The most promising advantage of FAST [[corner detector]] is its computational efficiency. Referring to its name, it is fast and indeed it is faster than many other well-known feature extraction methods, such as [[difference of Gaussians]] (DoG) used by [[Scale-invariant feature transform|SIFT]], [[Corner detection#The SUSAN corner detector|SUSAN]] and [[Harris affine region detector|Harris]]. Moreover when machine learning method is applied, a better performance could be achieved which takes less time and computational resources. FAST corner detector is very suitable for real-time video processing application because of high-speed performance.
== Segment test detector ==
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== Comparison with other detectors ==
In Rosten's research,<ref>Edward Rosten, [http://lanl.arXiv.org/pdf/0810.2434 FASTER and better: A machine learning approach to corner detection]</ref> FAST and FAST-ER detector are evaluated on several different datasets and compared with the [[DoG]], [[Harris affine region detector|Harris]], [[Harris-Laplace]], [[Shi-Tomasi]], and [[Corner detection#The SUSAN corner detector|SUSAN]] corner detectors.
The parameter settings for the detectors (other than FAST) are as follows:
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