Saddle: Fast and repeatable features with good coverage

Autoren Javier Aldana-Iuit
Dmytro Mishkin
Ondřej Chum
Jiří Matas
Editoren
Titel Saddle: Fast and repeatable features with good coverage
Typ Artikel
Journal Image and Vision Computing
Verlag Elsevier
DOI 10.1016/j.imavis.2019.08.011
ISSN 0262-8856
Monat September
Jahr 2019
SCCH ID# 19092
Abstract

A novel similarity-covariant feature detector that extracts points whose neighborhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile is presented. 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. Saddle is a fast approximation of Hessian detector as ORB, that implements the FAST detector, is for Harris detector. We propose to use the matching strategy called the first geometric inconsistent with binary descriptors that is suitable for our feature detector, including experiments with fix point descriptors hand-crafted and learned.

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. Compared to recently proposed deep-learning based interest point detectors and popular hand-crafted keypoint detectors, evaluated for repeatability in the ApolloScape dataset [1], the Saddle detectors shows the best performance in most of the street-level view sequences a.k.a. traversals.