paper critique & summarization : Distinctive Image Features from Scale-Invariant Keypoints

Title: Distinctive Image Features from Scale-Invariant Keypoints
Author: D. G. Lowe

This paper proposes a approach named "Scale Invariant Feature Transform(SIFT)", which transforms image data into scale-invariant coordinates relative to local features.

the approach has 4 major stages, which introduced as follow:

1. Scale-space extrema detection - uses the DoG function to find the potential interest points, by comparing the 26 neighbor points and be selected if the DoG is larger or smaller then all others.

2. Keypoint localization - a detailed model is fit to determine location and scale. To delete the candidate points which contrast is low or on the edge.

3. Orientation assignment - one or more orientations are assigned to each keypoint location based on local image gradient directions. And the image data are transformed by this orientation to make sure the invariant of rotation.

4. Keypoint descriptor - represents these points with the parameters of location, scale, and orientation which assigned to these points.

And the keypoint descriptors generated by the above stages are highly distinctive, which is invariant in rotation, scale, and viewpoint.