Features from accelerated segment test

The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006.

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 SIFT, SUSAN and Harris detectors.

Moreover, when machine learning techniques are applied, superior performance in terms of computation time and resources can be realised.

Firstly, corner detection with a given N is processed on a set of training images which are preferable from the target application domain.

Corners are detected through the simplest implementation which literally extracts a ring of 16 pixels and compares the intensity values with an appropriate threshold.

The state of each pixel, Sp→x must be in one of the following three states: Then choosing an x (same for all p) partitions P (the set of all pixels of all training images) into 3 different subsets, Pd, Ps, Pb where: Secondly, a decision tree algorithm, the ID3 algorithm is applied to the 16 locations in order to achieve the maximum information gain.

Notice that the corners detected using this decision tree algorithm should be slightly different from the results using segment test detector.

"Since the segment test does not compute a corner response function, non-maximum suppression can not be applied directly to the resulting features."