Introduction to BRIEF(Binary Robust Independent Elementary Features) | by Deepanshu Tyagi | Medium

Feature point descriptors are now at the core of many Computer Vision technologies, such as object recognition, 3D reconstruction, image retrieval, and camera localization. Since applications of these technologies have to handle ever more data or to run on mobile devices with limited computational resources, there is a growing need for local descriptors that are fast to compute, fast to match, and memory efficient. In this article, we use binary strings as an efficient feature point descriptor, which is called BRIEF. BRIEF is very fast both to build and to match.BRIEF easily outperforms other fast descriptors such as SURF and SIFT in terms of speed and terms of recognition rate in many cases. This is part of a 7-series Feature Detection and Matching. Other articles included After detecting keypoint we go on to compute a descriptor for every one of them. Feature descriptors encode interesting information into a series of numbers and act as a sort of numerical “fingerprint” that can be used

Feature point descriptors are now at the core of many Computer Vision technologies, such as object recognition, 3D reconstruction, image retrieval, and camera localization. Since applications of these technologies have to handle ever more data or to run on mobile devices with limited computational resources, there is a growing need for local descriptors that are fast to compute, fast to match, and memory efficient. In this article, we use binary strings as an efficient feature point descriptor, which is called BRIEF. BRIEF is very fast both to build and to match.BRIEF easily outperforms other fast