Binary Robust Independent Elementary Features (BRIEF)

Even though we have FAST to quickly detect the keypoints, we still have to use SIFT or SURF to compute the descriptors. We need a way to quickly compute the descriptors as well. This is where BRIEF comes into the picture. BRIEF is a method for extracting feature descriptors. It cannot detect the keypoints by itself, so we need to use it in conjunction with a keypoint detector. The good thing about BRIEF is that it's compact and fast.

Consider the following image:

BRIEF takes the list of input keypoints and outputs an updated list. So if you run BRIEF on this image, you will see something like this:

Following is the code:

import cv2

import numpy as np

gray_image = cv2.imread('input.jpg', 0)

# Initiate FAST detector
fast = cv2.FastFeatureDetector()

# Initiate BRIEF extractor
brief = cv2.DescriptorExtractor_create("BRIEF")

# find the keypoints with STAR
keypoints = fast.detect(gray_image, None)

# compute the descriptors with BRIEF
keypoints...