Speeded-UpRobust Features (SURF)
Herbert Bay a , Andreas Ess a , Tinne Tuytelaars b , and Luc Van Gool a,b
aETH Zurich, BIWI
Sternwartstrasse 7
CH-8092 Zurich
Switzerland
bK. U. Leuven, ESAT-PSI
Kasteelpark Arenberg 10
B-3001 Leuven
Belgium
Abstract
This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features).
SURF 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 (specifically, 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 encompasses a detailed description of the detector and descriptor and then explores the effect of the most important
parameters. We conclude the article with SURF’s application to two challenging, yet converse goals: camera calibration as a special
case of image registration, and object recognition. Our experiments underline SURF’s usefulness in a broad range of topics in
computer vision.
Key words:
interest points, local features, feature description, camera calibration, object recognition
PACS:
1. Introduction
The task of finding point correspondences between two
images of the same scene or object is part of many computer
vision applications. Image registration, camera calibration,
object recognition, and image retrieval are just a few.
The search for discrete image point correspondences can
be divided into three main steps. First, ‘interest points’
are selected at distinctive locations in the image, such as
corners, blobs, and T-junctions. The most valuable prop-
erty of an interest point detector is its repeatability. The
repeatability expresses the reliability of a detector