BRIEF: Binary Robust Independent Elementary Features Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua CVLab, EPFL, Lausanne, Switzerland

Contributions Pros: Cons: Compact, easy-computed, highly discriminative Fast matching using Hamming distance Good recognition performance Cons: More sensitive to image distortions and transformations, in particular to in-plane rotation and scale change

Related work Descriptors: SIFT, SURF, DAISY, etc Descriptor + Dimension Reduction (e.g. PCA, LDA, etc) Quantization Hashing (e.g. Locality Sensitive Hashing)

Method Binary test BRIEF descriptor For each S*S patch Smooth it Pick pixels using pre-defined binary tests

Smoothing kernels De-noising Gaussian kernels

Spatial arrangement of the binary tests (X,Y)~i.i.d. Uniform (X,Y)~i.i.d. Gaussian X~i.i.d. Gaussian , Y~i.i.d. Gaussian Randomly sampled from discrete locations of a coarse polar grid introducing a spatial quantization. and takes all possible values on a coarse polar grid containing points

Distance Distributions

Experiments

BRISK: Binary Robust Invariant Scalable Keypoints Stefan Leutenegger, Margarita Chli and Roland Y. Siegwart Autonomous Systems Lab, ETH Zurich

Contributions Combination of SIFT-like scale-space keypoint detection and BREIF-like descriptor Scale and rotation invariant

Method Scale-space keypoint detection

Sampling pattern

Local gradient All sampling-point pairs Short-distance pairings S and long-distance pairings L

Overall characteristic pattern direction Descriptor Rotation- and scale-normalization BRIEF-like Matching: Hamming distance

Experiments