InforLorV4, Main, Merge, bibRecord, 002355

Generic R-transform for invariant pattern representation

Identifieur interne : 002355 ( Main/Merge ); précédent : 002354; suivant : 002356

Generic R-transform for invariant pattern representation

Auteurs : Thai V. Hoang [France] ; Salvatore Tabbone [France]

Source :

RBID : Hal:inria-00593469

English descriptors

mix :
- Invariant representation, Noise robustness, R-signature, R-transform, Radon transform.

Abstract

The beneficial properties of the Radon transform make it an useful intermediate representation for the extraction of invariant features from pattern images for the purpose of indexing/matching. This paper revisits the problem with a generic view on a popular Radon-based pattern descriptor, the R-signature, bringing in a class of descriptors spatially describing patterns at all the directions and at different levels. The domain of this class and the selection of its representative are also discussed. Theoretical arguments validate the robustness of the generic R-signature to additive noise and experimental results show its effectiveness.

Url:

https://hal.inria.fr/inria-00593469

DOI: 10.1109/CBMI.2011.5972538

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 002597
to stream Hal, to step Curation: 002597
to stream Hal, to step Checkpoint: 001D83

Links to Exploration step

Hal:inria-00593469

Le document en format XML

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<front><div type="abstract" xml:lang="en">The beneficial properties of the Radon transform make it an useful intermediate representation for the extraction of invariant features from pattern images for the purpose of indexing/matching. This paper revisits the problem with a generic view on a popular Radon-based pattern descriptor, the R-signature, bringing in a class of descriptors spatially describing patterns at all the directions and at different levels. The domain of this class and the selection of its representative are also discussed. Theoretical arguments validate the robustness of the generic R-signature to additive noise and experimental results show its effectiveness.</div>
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Generic R-transform for invariant pattern representation

Generic R-transform for invariant pattern representation

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Abstract

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