BRIEF: Binary Robust Independent Elementary Features
Abstract
We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF.We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using the Hamming distance, which is very efficient to compute, instead of the L _2 norm as is usually done. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and U-SURF on standard benchmarks and show that it yields a similar or better recognition performance, while running in a fraction of the time required by either.
Cite
Text
Calonder et al. "BRIEF: Binary Robust Independent Elementary Features." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15561-1_56Markdown
[Calonder et al. "BRIEF: Binary Robust Independent Elementary Features." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/calonder2010eccv-brief/) doi:10.1007/978-3-642-15561-1_56BibTeX
@inproceedings{calonder2010eccv-brief,
title = {{BRIEF: Binary Robust Independent Elementary Features}},
author = {Calonder, Michael and Lepetit, Vincent and Strecha, Christoph and Fua, Pascal},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {778-792},
doi = {10.1007/978-3-642-15561-1_56},
url = {https://mlanthology.org/eccv/2010/calonder2010eccv-brief/}
}