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

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Presentation on theme: "BRIEF: Binary Robust Independent Elementary Features Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua CVLab, EPFL, Lausanne, Switzerland."— Presentation transcript:

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

2 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

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 1.Smooth it 2.Pick pixels using pre-defined binary tests

5 Smoothing kernels De-noising Gaussian kernels

6 Spatial arrangement of the binary tests 1.(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

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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

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