A novel similarity-covariant feature detector that extracts points whose neighbourhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile. The saddle condition is verified efficiently by intensity comparisons on two concentric rings that must have exactly two dark-to-bright and two bright-to-dark transitions satisfying certain geometric constraints. Experiments show that the Saddle features are general, evenly spread and appearing in high density in a range of images. The Saddle detector is among the fastest proposed. In comparison with detector with similar speed, the Saddle features show superior matching performance on number of challenging datasets.
@article{arxiv.1608.06800,
title = {In the Saddle: Chasing Fast and Repeatable Features},
author = {Javier Aldana-Iuit and Dmytro Mishkin and Ondrej Chum and Jiri Matas},
journal= {arXiv preprint arXiv:1608.06800},
year = {2016}
}