SURF: Speeded Up Robust Features

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SURF: Speeded Up Robust Features

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by Herbert Bay , Tinne Tuytelaars , Luc Van Gool

Venue:	ECCV
Citations:	838 - 12 self

BibTeX

@ARTICLE{Bay_surf:speeded,
    author = {Herbert Bay and Tinne Tuytelaars and Luc Van Gool},
    title = {SURF: Speeded Up Robust Features},
    journal = {ECCV},
    year = {}
}

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Abstract

Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust 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 descrip-tors (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. 1

Keyphrases

robust feature hessian matrix-based measure image convolution ro-bust feature novel scale distribution-based descriptor real-life object recognition application rotation-invariant interest point detector novel detection standard evaluation set surf strong performance experimental result integral image

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