BFROST: binary features from robust orientation segment tests accelerated on the GPU

Abstract

A fast local image feature detector and descriptor that is implementable on the GPU is proposed. This method is the first GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of our orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, a binary feature descriptor is proposed which is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory consumption. The proposed method demonstrates good robustness and very fast computation times, making it usable in real-time applications.