BRIEF: Computing a Local Binary Descriptor Very Fast

By Michael Calonder, Vincent Lepetit, Mustafa Özuysal, Tomasz Trzcinski, Christoph Strecha, Pascal Fua. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012

885 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8c557561cad4ef357bed3a96d83e15d01c322d1a

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Citations in the corpus

From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, background): “…binary feature descriptors designed for image matching, with noticeable ones in-10 cluding Binary Robust Independent Elementary Features (BRIEF) [26], Oriented FAST and Rotated BRIEF (ORB) [199], Binary Ro-bust Invariant Scalable Keypoints (BRISK) [115] and Fast Retina Keypoint (FREAK) [6].”
RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, methodology): “The ORB (Oriented FAST and Rotated BRIEF) [27] algorithm uses the features from accelerated segment test (FAST) [28] to extract feature points and utilises the directional binary robust independent elementary features (BRIEF) [29] algorithm for feature description.”
Incorporating visual adjectives for image classification (2016, background): “…Location and Orientation Histogram (GLOH) [10], Speeded Up Robust Features (SURF) [11], Binary Robust Independent Elementary Features (BRIEF) [12], DAISY descriptor [13] and Oriented FAST and Rotated BRIEF (ORB) [14], are also veri ﬁ ed ef ﬁ cient and robust in image classi ﬁ cation/…”
Face Recognition Systems: A Survey (2020, methodology): “• Binary robust independent elementary features (BRIEF) [30,57]: BRIEF is a binary descriptor that is simple and fast to compute.”
Omnidirectional 3D reconstruction in augmented Manhattan worlds (2014, methodology): “We employ the FAST corner detector [38] in combination with the BRIEF descriptor [39], which empirically led to the best results for our images which suffer from blur at the image boundaries and incidental noise due to small scratches in the mirror surface.”
Made to measure: Bespoke landmarks for 24-hour, all-weather localisation with a camera (2016, methodology): “These are then described with a local feature descriptor such as SIFT [10], SURF [11] or one of the binary descriptors [12][13][14][15].”
Visual Place Recognition: A Survey (2016, methodology): “Badino et al. [108] used whole-image descriptors based on SURF features known as WI-SURF to perform localization and BRIEF- Gist [109] used BRIEF features [91] in a similar whole-image fashion.”
Pose Estimation for Augmented Reality: A Hands-On Survey (2016, positive): “Matching using BRIEF descriptors [21] (see video1 and video2, available online).”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, methodology): “Notable methods along these lines include BRIEF [38], BRISK [24] and FREAK [25], where in all cases a binary descriptor vector is obtained by comparing the intensities of a number of pairs of pixels after applying a Gaussian smoothing to reduce the noise sensitivity.”
Learning to Match Aerial Images with Deep Attentive Architectures (2016, background): “Numerous descriptors have been developed within the community, such as SURF [5], BRIEF [8], and DAISY [36].”