This is our fifth post in the series about binary descriptors and here we will talk about the FREAK descriptor. This is the last descriptor that we’ll talk about as the next and final post in the series will give a performance evaluation of the different binary descriptors. Just a remainder – we had an introduction to binary descriptors and posts about BRIEF, ORB and BRISK.
This fourth post in our series about binary descriptors that will talk about the BRISK descriptor . We had an introduction to patch descriptors, an introduction to binary descriptors and posts about the BRIEF  and the ORB  descriptors.
We’ll start by showing the following figure that shows an example of using BRISK to match between real world images with viewpoint change. Green lines are valid matches, red circles are detected keypoints.
This third post in our series about binary descriptors that will talk about the ORB descriptor . We had an introduction to patch descriptors, an introduction to binary descriptors and a post about the BRIEF  descriptor.
We’ll start by showing the following figure that shows an example of using ORB to match between real world images with viewpoint change. Green lines are valid matches, red circles indicate unmatched points.
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).
So, if there no question about SIFT and SURF performance, why not use them in every application?
Lately, I’ve been reading a lot about BOW (Bag of Words) models  and I thought it would be nice to write a short post on the subject. The post is based on the slides from Li Fei-Fei taken from ICCV 2005 course about object detection:
As the name implies, the concept of BOW is actually taken from text analysis. The idea is to represent a document as a “bag” of important keywords, without ordering of the words (that’s why it’s a called “bag of words”, instead of a list for example).
In computer vision, the idea is similar. We represent an object as a bag of visual words – patches that described by a certain descriptor:
Since the next few posts will talk about binary descriptors, I thought it would be a good idea to post a short introduction to the subject of patch descriptors. The following post will talk about the motivation to patch descriptors, the common usage and highlight the Histogram of Oriented Gradients (HOG) based descriptors.
I think the best way to start is to consider one application of patch descriptors and to explain the common pipeline in their usage. Consider, for example, the application of image alignment: we would like to align two images of the same scene taken at slightly different viewpoints. One way of doing so is by applying the following steps:
Compute distinctive keypoints in both images (for example, corners).
Compare the keypoints between the two images to find matches.
Use the matches to find a general mapping between the images (for example, a homography).
Apply the mapping on the first image to align it to the second image.