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.

- Enforce invariance to scale: Compute Gaussian difference max, for

may different scales; non-maximum suppression, find local maxima:

keypoint candidates - Localizable corner: For each maximum fit quadratic function.

Compute center with sub-pixel accuracy by setting first derivative to

zero. - Eliminate edges: Compute ratio of eigenvalues, drop keypoints for which this ratio is larger than a threshold.
- 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. - 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). - 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.

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