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

2. 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

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

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

5. Smoothing kernels
De-noising
Gaussian kernels

6. 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

9. Distance Distributions

10. Experiments

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

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

13. Method
Scale-space keypoint detection

14. Sampling pattern

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

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

17. Experiments