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Scale Invariant Feature Transform with Irregular Orientation Histogram Binning

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44273

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44590

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45618

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

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

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Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-19DA-C
Zusammenfassung
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.