[Reading] Distinctive Image Features from Scale-Invariant Keypoints

This paper presents a method named SIFT for extracting distinctive invariant features (named SIFT) from images that providing a basis for object and scene recognition. SIFT is a carefully designed procedure with empirically determined parameters for the invariant and distinctive features.

SIFT has the following four stages (the first two is as a detector, the last two is as a descriptor):
(1) Scale-space extrema detection
Use a DOG function to identify potential interest points that are invariant to scale.
(2) Keypoint localization
Detailed fitting for sub-pixel accuracy and further selection based on stability.
(3) Orientation assignment
In short it is based on gradient directions, so the feature are orientation invariant.
(4) Keypoint descriptor
Create array of orientation histograms.

The SIFT keypoints are invariant to image scale and rotation and robust across a substantial range of affine distortion, addition of noise, and change in illumination.