SURF: Speeded Up Robust Features
Herbert Bay1, Tinne Tuytelaars2, and Luc Van Gool12
1 ETH Zurich
{bay, vangool}@vision.ee.ethz.ch
2 Katholieke Universiteit Leuven
{Tinne.Tuytelaars, Luc.Vangool}@esat.kuleuven.be
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 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.
1 Introduction
The task of finding correspondences between two images of the same scene or
object is part of many computer vision applications. Camera calibration, 3D
reconstruction, image registration, and object recognition are just a few. The
search for discrete image correspondences – the goal of this work – can be di-
vided into three main steps. First, ‘interest points’ are selected at distinctive
locations in the image, such as corners, blobs, and T-junctions. The most valu-
able property of an interest point detector is its repeatability, i.e. whether it
reliably finds the same interest points under different viewing conditions. Next,
the neighbourhood of every interest point is represented by a feature vector. This
descriptor has to be distinctive and, at the same time, robust to noise, detec-
tion errors, and geometric and photometric deformations. Finally, the descriptor
vectors are matched bet