Features from accelerated segment test: Difference between revisions

Revision as of 20:00, 10 November 2022

Revision as of 09:16, 12 November 2022

Line 1:

'''Features from accelerated segment test (FAST)''' is a [[corner detection]] method, which could be used to extract [[Feature (computer vision)|feature]] points and later used to track and map objects in many [[computer vision]] tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006.<ref>

{{cite book

|last1=Rosten |first1=Edward

Line 13:

|isbn=978-3-540-33832-1

}}

</ref> The most promising advantage of the FAST [[corner detector]] is its computational efficiency. Referring to its name, it is indeed faster than many other well-known feature extraction methods, such as [[difference of Gaussians]] (DoG) used by the [[Scale-invariant feature transform|SIFT]], [[Corner detection#The SUSAN corner detector|SUSAN]] and [[Harris affine region detector|Harris]] detectors. Moreover, when machine learning techniques are applied, superior performance in terms of computation time and resources can be realised. The FAST corner detector is very suitable for real-time video processing application because of this high-speed performance.

== Segment test detector ==

Line 46:

Secondly, a [[decision tree]] algorithm, the [[ID3 algorithm]] is applied to the 16 locations in order to achieve the maximum [[information gain]]. Let K_p be a boolean variable which indicates whether p is a corner, then the [[Entropy (information theory)|entropy]] of K_p is used to measure the information of p being a corner. For a set of pixels Q, the total entropy of K_Q (not normalized) is:

*H(Q) = ( c + n ) log₂( c + n ) - clog₂c - nlog₂n

** where c = |{ i ∈ Q: K_i is true}| (number of corners)

** where n = |{ i ∈ Q: K_i is false}| (number of non-corners)

Line 65:

== FAST-ER: Enhanced repeatability ==

FAST-ER detector is an improvement of the FAST detector using a [[metaheuristic]] algorithm, in this case [[simulated annealing]]. So that after the optimization, the structure of the decision tree would be optimized and suitable for points with high repeatability. However, since [[simulated annealing]] is a metaheurisic algorithm, each time the algorithm would generate a different optimized decision tree. So it is better to take efficiently large amount of iterations to find a solution that is close to the real optimal. According to Rosten, it takes about 200 hours on a [[Pentium 4]] at 3&nbsp;GHz which is 100 repeats of 100,000 iterations to optimize the FAST detector.

== Comparison with other detectors ==

Line 138:

|}

* Speed tests were performed on a 3.0&nbsp;GHz [[Pentium D|Pentium 4-D]] computer. The dataset are divided into a training set and a test set. The training set consists of 101 [[monochrome]] images with a resolution of 992×668 pixels. The test set consists of 4968 frames of [[monochrome]] 352×288 video. And the result is:

{| class="wikitable"

|-