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キーワード:
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要旨:
The SIFT (Scale Invariant Feature Transform) descriptor is a widely used method
for matching image features. However, perfect scale invariance can not be
achieved in practice because of sampling artefacts, noise in the image data,
and the fact that the computational effort limits the number of analyzed scale
space images. In this paper we propose a modification of the descriptor's
regular grid of orientation histogram bins to an irregular grid. The irregular
grid approach reduces the negative effect of scale error and significantly
increases the matching precision for image features. Results with a standard
data set are presented that show that the irregular grid approach outperforms
the original SIFT descriptor and other state-of-the-art extentions.
