In this post I will explain how to add a simple rotation invariance mechanism to the BRIEF descriptor, I will present evaluation results showing the rotation invariant BRIEF significantly outperforms regular BRIEF where visual geometric changes are present and finally I will post a C++ implementation integrated into OpenCV3.
Just as a reminder, we had a general post on local image descriptors, an introductory post to binary descriptors and a post presenting the BRIEF descriptor. We also had posts on other binary descriptors: ORB, BRISK and FREAK.
We’ll start by a visual example, displaying the correct matches between a pair of images of the same scene, taken from different angles – once with the original version of BRIEF (first image pair) and one with the proposed rotation invariant version of BRIEF (second image pair):
Correct matches when using the BRIEF descriptor
Correct matches when using the rotation invariant BRIEF descriptor
It can be seen that there are much more correct matches when using the proposed rotation invariant of the BRIEF descriptor.
Following the previous posts that provided both an introduction to patch descriptors in general and specifically to binary descriptors, it’s time to talk about the individual binary descriptors in more depth. This post will talk about the BRIEF descriptor and the following post will talk about ORB, BRISK and FREAK.
Why Binary Descriptors?
Following the previous post on descriptors, we’re now familiar with histogram of gradients (HOG) based patch descriptors. SIFT, SURF and GLOH have been around since 1999 and been used successfully in various applications, including image alignment, 3D reconstruction and object recognition. On the practicle side, OpenCV includes implementations of SIFT and SURF and Matlab packages are also available (check vlfeat for SIFT and extractFeatures in Matlab computer vision toolbox for SURF).
BRISK descriptor – sampling pairs
So, if there no question about SIFT and SURF performance, why not use them in every application?