This third post in our series about binary descriptors that will talk about the ORB descriptor [1]. We had an introduction to patch descriptors, an introduction to binary descriptors and a post about the BRIEF [2] 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.
ORB descriptor – An example of keypoints matching using ORB
Following the previous post on descriptors, we’re now familiar with histogram of gradients (HOG) based patch descriptors. SIFT[1], SURF[2] and GLOH[3] 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?
Gil's CV blog 