SIFT

SIFT(Scale Invariant Feature Transform)

Distinctive image features from scale-invariant keypoints. David G.
Lowe, International Journal of Computer Vision, 60, 2 (2004), pp.
91-110.

1. Enforce invariance to scale: Compute Gaussian difference max, for
   may different scales; non-maximum suppression, find local maxima:
   keypoint candidates
2. Localizable corner: For each maximum fit quadratic function.
   Compute center with sub-pixel accuracy by setting first derivative to
   zero.
3. Eliminate edges: Compute ratio of eigenvalues, drop keypoints for which this ratio is larger than a threshold.
4. Enforce invariance to orientation: Compute orientation, to achieve
   scale invariance, by finding the strongest second derivative direction
   in the smoothed image (possibly multiple orientations). Rotate patch so
   that orientation points up.
5. Compute feature signature: Compute a “gradient
   histogram” of the local image region in a 4×4 pixel region.
   Do this for 4×4 regions of that size. Orient so that largest
   gradient points up (possibly multiple solutions). Result: feature
   vector with 128 values (15 fields, 8 gradients).
6. Enforce invariance to illumination change and camera saturation:
   Normalize to unit length to increase invariance to illumination. Then
   threshold all gradients, to become invariant to camera saturation.
   (from st course)

Codes are in paper author’s website.