Transcription of SURF: Speeded Up Robust Features
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SURF: Speeded Up Robust Features
surf : Speeded Up Robust FeaturesHerbert Bay1, Tinne Tuytelaars2, and Luc Van Gool121 ETH Zurich{bay, Universiteit Leuven{ , this paper, we present a novel scale- and rotation-invariantinterest point detector and descriptor, coined surf ( Speeded Up Ro-bust Features ). It approximates or even outperforms previously proposedschemes with respect to repeatability, distinctiveness, and robustness, yetcan be computed and compared much is achieved by relying on integral images for image convolutions; bybuilding on the strengths of the leading existing detectors and descriptors(in casu, using a Hessian matrix-based measure for the detector, and adistribution-based descriptor); and by simplifying these methods to theessential. This leads to a combination of novel detection, description, andmatching steps. The paper presents experimental results on a standardevaluation set, as well as on imagery obtained in the context of a real-lifeobject recognition application.}}
3 Fast-Hessian Detector We base our detector on the Hessian matrix because of its good performance in computation time and accuracy. However, rather than using a different measure for selecting the location and the scale (as was done in the Hessian-Laplace detector [11]), we rely on the determinant of the Hessian for both. Given a point
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