BRISK: Binary Robust invariant scalable keypoints

By Stefan Leutenegger, Margarita Chli, Roland Yves Siegwart. Vision, 2011

3,772 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/86d27422aac2398cfe132ae8e312a6f2d190f754

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Planning and Decision-Making for Autonomous Vehicles (2018, background): “BRISK: Binary Robust Invariant Scalable Keypoints.”
Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces (2013, methodology): “ORB [15] and BRISK [10] speed-up feature detection and description by combining modiﬁcations of the FAST corner detector [14] and binary descriptors based on BRIEF [4] with scale and rotation invariance.”
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.”
Fusion++: Volumetric Object-Level SLAM (2018, methodology): “This approach is similar to our relocalisation method, which uses BRISK features [18] but augmented with depth.”
Comparative Evaluation of Binary Features (2012, background): “[8] and provides both scale and rotation invariance.”
RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, methodology): “We compare the detector of RIFT with other six well-known feature detectors, including FAST [28], Harris [32], Brisk [56], SIFT [15], SURF [24], and MSER [33].”
From Coarse to Fine: Robust Hierarchical Localization at Large Scale (2018, background): “Scalable localization often deals with the additional compute constrains by using features that are inexpensive to extract, store, and match together [8, 24, 37].”
FREAK: Fast Retina Keypoint (2012, methodology): “Ambai and Yoshida proposed a Compact And Realtime Descriptors (CARD) in [3] to extract the histogram of oriented gradient from the grid binning of SIFT or the logpolar binning of GLOH.”
HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors (2017, background): “For SIFT-based descriptors we include SIFT [20] and its variant RSIFT [2].”
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (2016, background): “In order to reduce the computational complexity, several lightweight binary descriptors have been recently proposed such as BRIEF [6], ORB [33], BRISK [22], and FREAK [1].”
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features (2019, methodology): “2a illustrates the common variations of this pipeline, from using hand-crafted detectors [7,19,29,31,33] and descriptors [7,9,24,29,44], replacing either the descriptor [6,54,55] or detector [49,68] with a learned alternative, or learning both the detector and descriptor [38,64].”
LIFT: Learned Invariant Feature Transform (2016, methodology): “…Harris-aﬃne detector [36], MROGH [37] with Harris-aﬃne detector, LIOP [38] with Harris-aﬃne detector, BiCE [39] with Edge Foci detector [19], BRISK [40], FREAK [41] with BRISK detector, VGG [23] (trained on ‘Liberty’) with SIFT detector; DeepDesc [10] with SIFT detector; PN-Net [25] with SIFT…”
Automatic dense visual semantic mapping from street-level imagery (2012, neutral): “computationally efficient binary features like [3], [16].”
A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK (2018, methodology): “put forward Binary Robust Invariant Scalable Keypoints (BRISK) in 2011 [18], which detects corners using AGAST algorithm and filters them with FAST Corner score while searching for maxima in the scale space pyramid.”
Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight (2017, methodology): “Between OKVIS and VINS-MONO, OKVIS has more CPU usage mainly because it uses Harris corner detector [35] and BRISK [36] descriptor for both temporal and stereo matching.”