BRIEF: Binary Robust IndependentElementary Features

Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua

CVLab, EPFL, Lausanne, Switzerland

Contributions

• Pros:• 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 patch1. Smooth it

2. Pick pixels using pre-defined binary tests

Smoothing kernels

• De-noising• Gaussian kernels

Spatial arrangement of the binary tests1. (X,Y)~i.i.d. Uniform

2. (X,Y)~i.i.d. Gaussian

3. X~i.i.d. Gaussian , Y~i.i.d. Gaussian

4. Randomly sampled from discrete locations of a coarse polar grid introducing a spatial quantization.

5. 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