A comparison of affine region detectors

DMCA

A comparison of affine region detectors (2005)

Cached

Download Links

Other Repositories/Bibliography

CiteULike
DBLP

by K. Mikolajczyk , T. Tuytelaars , C. Schmid , A. Zisserman , J. Matas , F. Schaffalitzky , T. Kadir , L. Van Gool

Venue:	International Journal of Computer Vision
Citations:	364 - 19 self

BibTeX

@ARTICLE{Mikolajczyk05acomparison,
    author = {K. Mikolajczyk and T. Tuytelaars and C. Schmid and A. Zisserman and J. Matas and F. Schaffalitzky and T. Kadir and L. Van Gool},
    title = {A comparison of affine region detectors},
    journal = {International Journal of Computer Vision},
    year = {2005},
    volume = {65},
    pages = {2005}
}

OpenURL

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. 1

Keyphrases

affine region detector future detector reference test set six type test image van gool imaging condition hessian point affine covariant region detector stable extremal region edge-based region detector intensity extremum salient region affine normalization image compression performance software

DMCA