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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.}}

SURF: Speeded Up Robust Features 5 Fig.1. Left to right: the (discretised and cropped) Gaussian second order partial derivatives in y-direction and xy-direction, and our approximations thereof using box filters. The grey regions are equal to zero. applied to the rectangular regions are kept simple for computational efficiency,

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