%0 Journal Article %1 Mikolajczyk05acomparison %A Mikolajczyk, K. %A Tuytelaars, T. %A Schmid, C. %A Zisserman, A. %A Matas, J. %A Schaffalitzky, F. %A Kadir, T. %A Gool, L. Van %D 2005 %J International Journal of Computer Vision %K comparison detector feature %P 2005 %T A comparison of affine region detectors %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.595 %V 65 %X 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

@article{Mikolajczyk05acomparison, 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}, added-at = {2012-10-07T19:24:20.000+0200}, author = {Mikolajczyk, K. and Tuytelaars, T. and Schmid, C. and Zisserman, A. and Matas, J. and Schaffalitzky, F. and Kadir, T. and Gool, L. Van}, biburl = {https://www.bibsonomy.org/bibtex/2fececb7d3e9e64eccc901ef8cad8ef1c/daill}, description = {CiteSeerX — A comparison of affine region detectors}, interhash = {bbbb59b7bd0b7ed708e150497d0f12e4}, intrahash = {fececb7d3e9e64eccc901ef8cad8ef1c}, journal = {International Journal of Computer Vision}, keywords = {comparison detector feature}, pages = 2005, timestamp = {2012-10-07T19:24:20.000+0200}, title = {A comparison of affine region detectors}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.595}, volume = 65, year = 2005 }