Fast Robust Invariant Feature

24th British Machine Vision Conference (BMVC 2013)

Zhenhua Wang, Bin Fan and Fuchao Wu



Establishing robust visual correspondences is a fundamental component of many computer vision applications. However, it is very challenging to obtain high quality fea- tures while maintaining a low computational cost. This paper aims to tackle this problem by adopting a novel Fast Robust Invariant Feature (FRIF) for both feature detection and description. The basic idea is to employ a fast approximated LoG detector to select scale-invariant keypoints and incorporate local pattern and inter-pattern information to construct distinctive binary descriptors. A comprehensive evaluation on standard dataset shows that FRIF achieves quite a high performance with a computation time comparable to state-of-the-art real-time features.

Timing Results

For more detailed results, please refer to the paper



Paper: pdf
Poster: jpg
BitTex: bib


News: The code is hosted on github. Try it now: FRIF code

If you have any questions about this paper, please contact the author (wzh[at]