NDLI: HALF-SIFT: High-Accurate Localized Features for SIFT

Content Provider	IEEE Xplore Digital Library
Author	Cordes, K. Muller, O. Rosenhahn, B. Ostermann, J.
Copyright Year	2009
Description	Author affiliation: Inst. fur Informationsverarbeitung (TNT), Leibniz Univ. Hannover, Hannover, Germany (Cordes, K.; Muller, O.; Rosenhahn, B.; Ostermann, J.)
Abstract	In this paper, the accuracy of feature points in images detected by the scale invariant feature transform (SIFT) is analyzed. It is shown that there is a systematic error in the feature point localization. The systematic error is caused by the improper subpel and subscale estimation, an interpolation with a parabolic function. To avoid the systematic error, the detection of high-accurate localized features (HALF) is proposed. We present two models for the localization of a feature point in the scale-space, a Gaussian and a Difference of Gaussians based model function. For evaluation, ground truth image data is synthesized to experimentally prove the systematic error of SIFT and to show that the error is eliminated using HALF. Experiments with natural image data show that the proposed methods increase the accuracy of the feature point positions by 13.9% using the Gaussian and by 15.6% using the Difference of Gaussians model.
Starting Page	31
Ending Page	38
File Size	956219
Page Count	8
File Format	PDF
ISBN	9781424439942
ISSN	21607508
DOI	10.1109/CVPRW.2009.5204283
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Computer vision Interpolation Image analysis Image processing Layout Detectors Cameras Application software Information analysis
Content Type	Text
Resource Type	Article

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