Detects keypoints and computes their descriptors

[keypoints, descriptors] = obj.detectAndCompute(img)
[...] = obj.detectAndCompute(..., 'OptionName',optionValue, ...)

Input

• img Image, input 8-bit grayscale image.

Output

• keypoints The detected keypoints. A 1-by-N structure array with 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); it's in [0,360) degrees and 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 class (if the keypoints need to be clustered by an object they belong to).
• descriptors Computed descriptors. Output concatenated vectors of descriptors. Each descriptor is a 64- or 128-element vector, as returned by cv.SURF.descriptorSize, so the total size of descriptors will be numel(keypoints) * obj.descriptorSize(). A matrix of size N-by-(64/128) of class single, one row per keypoint.

Options

• Mask optional mask specifying where to look for keypoints. Not set by default.
• Keypoints If passed, then the method will use the provided vector of keypoints instead of detecting them, and the algorithm just computes their descriptors.

The function is parallelized with the TBB library.