Features from accelerated segment test: Difference between revisions

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*Condition 1: A set of N contiguous pixels S, ∀ x ∈ S, the intensity of x (I<sub>x</sub>) > I<sub>p</sub> + threshold t
*Condition 2: A set of N contiguous pixels S, ∀ x ∈ S, I<sub>x</sub> < I<sub>p</sub> - 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 the 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 ==