SURF: Speeded Up Robust Features
Herbert Bay, Tinne Tuytelaars, Luc Van Gool and Luc Van Gool
In: 9th European Conference on Computer Vision, 7-13 May 2006, Graz, Austria.
In this paper, we present a novel scale- and rotation-invariant
interest point detector and descriptor, coined SURF (Speeded Up Robust
Features). It approximates or even outperforms previously proposed
schemes with respect to repeatability, distinctiveness, and robustness, yet
can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions;
by building on the strengths of the leading existing detectors and descriptors
(in casu, using a Hessian matrix-based measure for the detector, and
a distribution-based descriptor); and by simplifying these methods to the
essential. This leads to a combination of novel detection, description, and
matching steps. The paper presents experimental results on a standard
evaluation set, as well as on imagery obtained in the context of a real-life
object recognition application. Both show SURF’s strong performance.
|EPrint Type:||Conference or Workshop Item (Poster)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Tinne Tuytelaars|
|Deposited On:||18 August 2006|