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SURF: Speeded Up Robust Features

Herbert Bay

, Tinne Tuytelaars

, and Luc Van Gool

1,2

ETH Zurich

{bay, vangool}@vision.ee.ethz.ch

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 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 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 between diﬀerent images. The matching is often based on a

distance between the vectors, e.g. the Mahanalobis or Euclidean distance. The

dimension of the descriptor has a direct impact on the time this takes, and a

lower number of dimensions is therefore desirable.

It has been our goal to develop both a detector and descriptor, which in

comparison to the state-of-the-art are faster to compute, while not sacriﬁcing

performance. In order to succeed, one has to strike a balance between the above

A. Leonardis, H. Bischof, and A. Pinz (Eds.): ECCV 2006, Part I, LNCS 3951, pp. 404–417, 2006.

 Springer-Verlag Berlin Heidelberg 2006

SURF: Speeded Up Robust Features 405

requirements, like reducing the descriptor’s dimension and complexity, while

keeping it suﬃciently distinctive.

A wide variety of detectors and descriptors have already been proposed in

the literature (e.g. [1, 2, 3,4, 5, 6]). Also, detailed comparisons and evaluations on

benchmarking datasets have been performed [7, 8, 9]. While constructing our fast

detector and descriptor, we built on the insights gained from this previous work

in order to get a feel for what are the aspects contributing to performance. In

our experiments on benchmark image sets as well as on a real object recognition

application, the resulting detector and descriptor are not only faster, but also

more distinctive and equally repeatable.

When working with local features, a ﬁrst issue that needs to be settled is

the required level of invariance. Clearly, this depends on the expected geomet-

ric and photometric deformations, which in turn are determined by the possible

changes in viewing conditions. Here, we focus on scale and image rotation invari-

ant detectors and descriptors. These seem to oﬀer a good compromise between

feature complexity and robustness to commonly occurring deformations. Skew,

anisotropic scaling, and perspective eﬀects are assumed to be second-order ef-

fects, that are covered to some degree by the overall robustness of the descriptor.

As also claimed by Lowe [2], the additional complexity of full aﬃne-invariant fea-

tures often has a negative impact on their robustness and does not pay oﬀ, unless

really large viewpoint changes are to be expected. In some cases, even rotation

invariance can be left out, resulting in a scale-invariant only version of our de-

scriptor, which we refer to as ’upright SURF’ (U-SURF). Indeed, in quite a few

applications, like mobile robot navigation or visual tourist guiding, the camera

often only rotates about the vertical axis. The beneﬁt of avoiding the overkill of

rotation invariance in such cases is not only increased speed, but also increased

discriminative power. Concerning the photometric deformations, we assume a

simple linear model with a scale factor and oﬀset. Notice that our detector and

descriptor don’t use colour.

The paper is organised as follows. Section 2 describes related work, on which

our results are founded. Section 3 describes the interest point detection scheme.

In section 4, the new descriptor is presented. Finally, section 5 shows the exper-

imental results and section 6 concludes the paper.

2 Related Work

Interest Point Detectors. The most widely used detector probably is the Har-

ris corner detector [10], proposed back in 1988, based on the eigenvalues of the

second-moment matrix. However, Harris corners are not scale-invariant. Lin-

deberg introduced the concept of automatic scale selection [1]. This allows to

detect interest points in an image, each with their own characteristic scale.

He experimented with both the determinant of the Hessian matrix as well as

the Laplacian (which corresponds to the trace of the Hessian matrix) to detect

blob-like structures. Mikolajczyk and Schmid reﬁned this method, creating ro-

bust and scale-invariant feature detectors with high repeatability, which they

406 H. Bay, T. Tuytelaars, and L. Van Gool

coined Harris-Laplace and Hessian-Laplace [11]. They used a (scale-adapted)

Harris measure or the determinant of the Hessian matrix to select the location,

and the Laplacian to select the scale. Focusing on speed, Lowe [12] approxi-

mated the Laplacian of Gaussian (LoG) by a Diﬀerence of Gaussians (DoG)

ﬁlter.

Several other scale-invariant interest point detectors have been proposed. Ex-

amples are the salient region detector proposed by Kadir and Brady [13], which

maximises the entropy within the region, and the edge-based region detector pro-

posed by Jurie et al. [14]. They seem less amenable to acceleration though. Also,

several aﬃne-invariant feature detectors have been proposed that can cope with

longer viewpoint changes. However, these fall outside the scope of this paper.

By studying the existing detectors and from published comparisons [15, 8],

we can conclude that (1) Hessian-based detectors are more stable and repeat-

able than their Harris-based counterparts. Using the determinant of the Hessian

matrix rather than its trace (the Laplacian) seems advantageous, as it ﬁres less

on elongated, ill-localised structures. Also, (2) approximations like the DoG can

bring speed at a low cost in terms of lost accuracy.

Feature Descriptors. An even larger variety of feature descriptors has been

proposed, like Gaussian derivatives [16], moment invariants [17], complex fea-

tures [18, 19], steerable ﬁlters [20], phase-based local features [21], and descrip-

tors representing the distribution of smaller-scale features within the interest

point neighbourhood. The latter, introduced by Lowe [2], have been shown to

outperform the others [7]. This can be explained by the fact that they capture

a substantial amount of information about the spatial intensity patterns, while

at the same time being robust to small deformations or localisation errors. The

descriptor in [2], called SIFT for short, computes a histogram of local oriented

gradients around the interest point and stores the bins in a 128-dimensional

vector (8 orientation bins for each of the 4 × 4 location bins).

Various reﬁnements on this basic scheme have been proposed. Ke and Suk-

thankar [4] applied PCA on the gradient image. This PCA-SIFT yields a 36-

dimensional descriptor which is fast for matching, but proved to be less distinc-

tive than SIFT in a second comparative study by Mikolajczyk et al. [8] and slower

feature computation reduces the eﬀect of fast matching. In the same paper [8],

the authors have proposed a variant of SIFT, called GLOH, which proved to be

even more distinctive with the same number of dimensions. However, GLOH is

computationally more expensive.

The SIFT descriptor still seems to be the most appealing descriptor for prac-

tical uses, and hence also the most widely used nowadays. It is distinctive and

relatively fast, which is crucial for on-line applications. Recently, Se et al. [22]

implemented SIFT on a Field Programmable Gate Array (FPGA) and improved

its speed by an order of magnitude. However, the high dimensionality of the de-

scriptor is a drawback of SIFT at the matching step. For on-line applications

on a regular PC, each one of the three steps (detection, description, matching)

should be faster still. Lowe proposed a best-bin-ﬁrst alternative [2] in order to

speed up the matching step, but this results in lower accuracy.

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kerrity

2016-10-09

求了很久的资源，感谢楼主

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