There are several feature-detetcting algorithms, which work really well. But when looking from a real-time application point of view, they are not fast enough. As a solution to this, FAST (Features from Accelerated Segment Test) algorithm was proposed by Edward Rosten and Tom Drummond in the year 2006 in their paper “Machine learning for high-speed corner detection” .
12 point segment test corner detection in an image patch
The highlighted squares are the pixels used in the corner detection. The pixel at p is the center of a candidate corner. The arc is indicated by the dashed line passes through 12 contiguous pixels which are brighter than p by more than the threshold.
Although it is several times faster than other existing corner detectors, still it is not robust to high levels of noise. It is dependant on a threshold.
It is called in the same way as any other feature detector in OpenCV. One can specify the threshold, whether non-maximum suppression to be applied or not, the neighborhood to be used or not, etc.
For the neighborhood, three flags are defined,
cv2.FAST_FEATURE_DETECTOR_TYPE_5_8
cv2.FAST_FEATURE_DETECTOR_TYPE_7_12
cv2.FAST_FEATURE_DETECTOR_TYPE_9_16
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