Question

Is SIFT a matching approach to replace ZNCC and NCC
or SIFT just provides input to NCC, in other words SIFT is proposed to be used as an alternative to Harris corner detection algorithm?

Answer 1

SIFT is actually a detection, description, and matching pipeline which is proposed by David Lowe. The reason for its popularity is that it works quite well out of the box.

The detection step of SIFT (which points in the image are interesting), comparable to the Harris corner detector that you mentioned, consists of a Difference of Gaussians detector. This detector is a center surround filter and is applied to a scale space pyramid (also applied in things like pyramidal LK tracking) to detect a maximal scale space response.

The description step (what distinguishes this region) then builds histograms of gradients in rectangular bins with several scales centered around the maximal response scale. This is meant as more descriptive and robust to illumination changes etc. than things like raw pixel values, color histograms, etc. There is also a normalization of dominant orientation to get in-plane rotational invariance.

The matching step (for a given descriptor/patch, which out of a pile of descriptors/patches is closest) for SIFT consist of a nearest distance ratio metric which tests for the ratio of distances between the closest match and second closest match. The idea is that if the ratio is low, then the first is much better than the second, thus you should make the match. Else, first and second is about equal and you should reject the match as noise, etc. can easily generate a false match in this scenario. This works better than Euclidean distance in practice. Though for large databases, you’ll need vector quantization etc. to keep this working accurately and efficiently.

Overall, I’d argue that the SIFT descriptor/match is a much better/robust approach than NCC/ZNCC though you do pay for it in computational load.