In the previous recipe, we learned how to describe a keypoint using rich descriptors extracted from the image intensity gradient. These descriptors are floating-point vectors that have a dimension of 64
, 128
, or sometimes even longer. This makes them costly to manipulate. In order to reduce the memory and computational load associated with these descriptors, the idea of using binary descriptors has been recently introduced. The challenge here is to make them easy to compute and yet keep them robust to scene and viewpoint changes. This recipe describes some of these binary descriptors. In particular, we will look at the ORB and BRISK descriptors for which we presented their associated feature point detectors in Chapter 8, Detecting Interest Points.

OpenCV Computer Vision Application Programming Cookbook
By :

OpenCV Computer Vision Application Programming Cookbook
By:
Overview of this book
Table of Contents (18 chapters)
OpenCV Computer Vision Application Programming Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Playing with Images
Manipulating Pixels
Processing Color Images with Classes
Counting the Pixels with Histograms
Transforming Images with Morphological Operations
Filtering the Images
Extracting Lines, Contours, and Components
Detecting Interest Points
Describing and Matching Interest Points
Estimating Projective Relations in Images
Processing Video Sequences
Index
Customer Reviews