Papers › In the Saddle: Chasing Fast and Repeatable Features
In the Saddle: Chasing Fast and Repeatable Features
Javier Aldana-Iuit, Dmytro Mishkin, Ondrej Chum, Jiri Matas
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.
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