Orthogonal Affine Invariants from Gaussian-Hermite Moments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00508021" target="_blank" >RIV/67985556:_____/19:00508021 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-29891-3_36" target="_blank" >http://dx.doi.org/10.1007/978-3-030-29891-3_36</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-29891-3_36" target="_blank" >10.1007/978-3-030-29891-3_36</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Orthogonal Affine Invariants from Gaussian-Hermite Moments

Popis výsledku v původním jazyce

We propose a new kind of moment invariants with respect to an affine transformation. The new invariants are constructed in two steps. First, the affine transformation is decomposed into scaling, stretching and two rotations. The image is partially normalized up to the second rotation, and then rotation invariants from Gaussian-Hermite moments are applied. Comparing to the existing approaches – traditional direct affine invariants and complete image normalization – the proposed method is more numerically stable. The stability is achieved thanks to the use of orthogonal Gaussian-Hermite moments and also due to the partial normalization, which is more robust to small changes of the object than the complete normalization. Both effects are documented in the paper by experiments. Better stability opens the possibility of calculating affine invariants of higher orders with better discrimination power. This might be useful namely when different classes contain similar objects and cannot be separated by low-order invariants.

Název v anglickém jazyce

Orthogonal Affine Invariants from Gaussian-Hermite Moments

Popis výsledku anglicky

We propose a new kind of moment invariants with respect to an affine transformation. The new invariants are constructed in two steps. First, the affine transformation is decomposed into scaling, stretching and two rotations. The image is partially normalized up to the second rotation, and then rotation invariants from Gaussian-Hermite moments are applied. Comparing to the existing approaches – traditional direct affine invariants and complete image normalization – the proposed method is more numerically stable. The stability is achieved thanks to the use of orthogonal Gaussian-Hermite moments and also due to the partial normalization, which is more robust to small changes of the object than the complete normalization. Both effects are documented in the paper by experiments. Better stability opens the possibility of calculating affine invariants of higher orders with better discrimination power. This might be useful namely when different classes contain similar objects and cannot be separated by low-order invariants.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20206 - Computer hardware and architecture

Projekt

<a href="/cs/project/GA18-07247S" target="_blank" >GA18-07247S: Metody a algoritmy pro analýzu obrazů vektorových a tenzorových polí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computer Analysis of Images and Patterns : CAIP 2019 International Workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019, Proceedings

ISBN

978-3-030-29929-3

ISSN

—

e-ISSN

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Počet stran výsledku

12

Strana od-do

413-424

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Salerno

Datum konání akce

2. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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