---
title: SIFT and Feature Matching
categories: session
---

# Briefing

In feature tracking, we use the time derivative to track
each feature point once detected.

In feature matching, we compare images which are not necessarily 
related on the time axis.  In other words, we need to compare
features directly, and pair corresponding features in two independent
images.

Two identify features, we use feature descriptors.
One of the most popular descriptors is called SIFT -
Scale Invariant Feature Transform.

Unfortunately, this is not described in the textbook.
For details, one can refer to 
[Szeliski's](https://szeliski.org/Book/) textbook,
which is currently available as drafts for the second edition.
SIFT is described on page 435ff in the version of 30 September this year..

The principle used by SIFT is to gather statistics from the neighbourhood
around the feature point, and use this as an identifier.

# Exercises

In the exercise, you should use the OpenCV implementation of SIFT.

# Debrief