PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A comparison of affine region detectors
K. Mikolajzyk, Tinne Tuytelaars, Cordelia Schmid, Andrew Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, Luc Van Gool and Luc Van Gool
International journal on computer vision 2005.

Abstract

The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris [24, 34] and Hessian points [24], as proposed by Mikolajczyk and Schmid and by Schaffalitzky and Zisserman; a detector of ‘maximally stable extremal regions’, proposed by Matas et al. [21]; an edge-based region detector [45] and a detector based on intensity extrema [47], proposed by Tuytelaars and Van Gool; and a detector of ‘salient regions’, proposed by Kadir, Zisserman and Brady [12]. The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1616
Deposited By:Tinne Tuytelaars
Deposited On:28 November 2005