Detects corners using the AGAST algorithm

keypoints = cv.AGAST(im)
keypoints = cv.AGAST(im, 'OptionName', optionValue, ...)

Input

• im 8-bit grayscale image where keypoints (corners) are to be detected.

Output

• keypoints Keypoints detected on the image. A 1-by-N structure array. It has the following fields:
  • pt coordinates of the keypoint [x,y]
  • size diameter of the meaningful keypoint neighborhood
  • angle computed orientation of the keypoint (-1 if not applicable). Its possible values are in a range [0,360) degrees. It is measured relative to image coordinate system (y-axis is directed downward), i.e in clockwise.
  • response the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling.
  • octave octave (pyramid layer) from which the keypoint has been extracted.
  • class_id object id that can be used to clustered keypoints by an object they belong to.

Options

• Threshold Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. default 10.
• NonmaxSuppression If it is true, non-maximum supression is applied to detected corners (keypoints). default true.
• Type one of the four neighborhoods as defined in the paper:
  • AGAST_5_8
  • AGAST_7_12d
  • AGAST_7_12s
  • OAST_9_16 (default)

Detects corners using the AGAST algorithm by [mair2010].

For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. The 32-bit binary tree tables were generated automatically from original code using perl script.

References

[mair2010]:

E. Mair, G. D. Hager, D. Burschka, M. Suppa, and G. Hirzinger. "Adaptive and generic corner detection based on the accelerated segment test". In "European Conference on Computer Vision (ECCV'10)", Sept 2010. Link.