Features from accelerated segment test: Difference between revisions

Revision as of 01:32, 23 January 2020

Revision as of 15:18, 23 February 2020

Line 10:

== Segment test detector ==

[[File:FAST Corner Detector.jpg|thumb|The pixels used by the FAST corner detector]]

FAST corner detector uses a circle of 16 pixels (a [[Midpoint circle algorithm|Bresenham circle]] of radius 3) to classify whether a candidate point p is actually a corner. Each pixel in the circle is labeled from integer number 1 to 16 clockwise. If a set of N contiguous pixels in the circle are all brighter than the intensity of candidate pixel p (denoted by I_p) plus a threshold value t or all darker than the intensity of candidate pixel p minus threshold value t, then p is classified as corner. The conditions can be written as:

*Condition 1: A set of N contiguous pixels S, ∀<math>\forall x ∈\in S</math>, the intensity of x (I_x) > I_p + threshold, or <math>I_x > I_p + t</math>

*Condition 2: A set of N contiguous pixels S, ∀<math>\forall x ∈\in S</math>, I<sub>x</submath>I_x < I_p I<sub>p- t</submath> - t

So when either of the two conditions is met, candidate p can be classified as a corner. There is a tradeoff of choosing N, the number of contiguous pixels and the threshold value t. On one hand the number of detected corner points should not be too many, on the other hand, the high performance should not be achieved by sacrificing computational efficiency. Without the improvement of [[machine learning]], N is usually chosen as 12. A high-speed test method could be applied to exclude non-corner points.

== High-speed test ==