Selection of Keypoints in 2D Images Using F-Transform

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F22%3AA2302G4A" target="_blank" >RIV/61988987:17610/22:A2302G4A - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-08974-9_33" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-08974-9_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-08974-9_33" target="_blank" >10.1007/978-3-031-08974-9_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Selection of Keypoints in 2D Images Using F-Transform

Popis výsledku v původním jazyce

We focus on a new fast and robust algorithm for selecting keypoints in 2D images using the following techniques: image regularization, selection of spaces with closeness, and design of the corresponding graph Laplacians. Then, the representative keypoints are local extrema in the image after the Laplacian operator is applied. The convolution kernels, used for regularization, are extracted from the uniform partition of the image domain, and the graph Laplacian is constructed using the theory of F0-transforms. Empirically, we show that sequences of F-transform kernels that correspond to different regularization levels share the property that they do not introduce new local extrema into the image under convolution. This justifies the computation of keypoints as points where local extrema are reached and allows them to be classified according to the values of the local extrema. We show that the extracted key points are representative in the sense that they allow a good approximate reconstruction of the original image from the calculated components of the F-transform taken from different convolutions. In addition, we show that the proposed algorithm is resistant to Gaussian noise.

Název v anglickém jazyce

Selection of Keypoints in 2D Images Using F-Transform

Popis výsledku anglicky

We focus on a new fast and robust algorithm for selecting keypoints in 2D images using the following techniques: image regularization, selection of spaces with closeness, and design of the corresponding graph Laplacians. Then, the representative keypoints are local extrema in the image after the Laplacian operator is applied. The convolution kernels, used for regularization, are extracted from the uniform partition of the image domain, and the graph Laplacian is constructed using the theory of F0-transforms. Empirically, we show that sequences of F-transform kernels that correspond to different regularization levels share the property that they do not introduce new local extrema into the image under convolution. This justifies the computation of keypoints as points where local extrema are reached and allows them to be classified according to the values of the local extrema. We show that the extracted key points are representative in the sense that they allow a good approximate reconstruction of the original image from the calculated components of the F-transform taken from different convolutions. In addition, we show that the proposed algorithm is resistant to Gaussian noise.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Information Processing and Management of Uncertainty in Knowledge-Based Systems

ISBN

978-3-031-08974-9

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

418-430

Název nakladatele

Springer

Místo vydání

—

Místo konání akce

Milano

Datum konání akce

11. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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