SIFT Keypoint Location

Question

I am currently working in SIFT, I had generated the difference of Gaussian and the extrema image layers. Can anyone explain to me how to use Hessian matrix to eliminate the low contrast keypoint?

Answer 1

A good keypoint is a corner. This comes from the Harris corner work and the Good features to track (KLT) papers first, then emphasized by the Mikolajczyk and Schmid paper.

Intuitively, a corner is a good feature because it is an intersection of two lines, while a single line segment can be moved along its direction, thus causing a less accurate localization. A line segment is an edge, i.e., a first order derivative (gradient). A corner is an edge that changes its direction abruptly. This is measured by a second order derivative, hence the use of the Hessian matrix that contains the values of the directional second derivatives.