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Conference Paper

Scale Invariant Feature Transform with Irregular Orientation Histogram Binning

MPS-Authors
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Cui,  Yan
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons44590

Hasler,  Nils
Computer Graphics, MPI for Informatics, Max Planck Society;

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Thormählen,  Thorsten
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45449

Seidel,  Hans-Peter       
Computer Graphics, MPI for Informatics, Max Planck Society;

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https://rdcu.be/dJkon
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Citation

Cui, Y., Hasler, N., Thormählen, T., & Seidel, H.-P. (2009). Scale Invariant Feature Transform with Irregular Orientation Histogram Binning. In Image Analysis and Recognition (pp. 258-267). Heidelberg, Germany: Springer.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-19DA-C
Abstract
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.