Draft:Binary Robust Independent Elementary Features
Image feature descriptor
From Wikipedia, the free encyclopedia
Binary Robust Independent Elementary Features (BRIEF) is an feature descriptor used in computer vision for object detection.[1] Its goal is to replace SIFT and SURF in environments with limited resources. This is done by using a binary string for the descriptor, which can be compared efficiently using Hamming distance. BRIEF itself is merely a feature descriptor, to use it for object detection it needs to be paired with a separate algorithm for key point detection like FAST in ORB.[2]
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Algorithm
The descriptor for an image region is built by comparing the intensity of pixels within the region. The region is smoothed before comparing pixels in order to make the descriptor more robust to noise. The comparisons are done using the following function:
where is the intensity of the smoothed pixel, and and are coordinates of the pixels to be compared.[1] This is then done for a set of pairs of , resulting in a -bit descriptor. These pairs can be selected by sampling from a two-dimensional probability distribution.
