Robust Multivariate Density Estimation under Gaussian Noise

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00524621" target="_blank" >RIV/67985556:_____/20:00524621 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11045-020-00702-7" target="_blank" >https://link.springer.com/article/10.1007/s11045-020-00702-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11045-020-00702-7" target="_blank" >10.1007/s11045-020-00702-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Multivariate Density Estimation under Gaussian Noise

Popis výsledku v původním jazyce

Observation of random variables is often corrupted by additive Gaussian noise. Noisereducing data processing is time-consuming and may introduce unwanted artifacts. In thisnpaper, a novel approach to description of random variables insensitive with respect to Gaussian noise is presented. The proposed quantities represent the probability density function of the variable to be observed, while noise estimation, deconvolution or denoising are avoided. Projection operators are constructed, that divide the probability density function into a non-Gaussian and a Gaussian part. The Gaussian part is subsequently removed by modifying the characteristic function to ensure the invariance. The descriptors are based on the moments of the probability density function of the noisy random variable. The invariance property and the performance of the proposed method are demonstrated on real image data.

Název v anglickém jazyce

Robust Multivariate Density Estimation under Gaussian Noise

Popis výsledku anglicky

Observation of random variables is often corrupted by additive Gaussian noise. Noisereducing data processing is time-consuming and may introduce unwanted artifacts. In thisnpaper, a novel approach to description of random variables insensitive with respect to Gaussian noise is presented. The proposed quantities represent the probability density function of the variable to be observed, while noise estimation, deconvolution or denoising are avoided. Projection operators are constructed, that divide the probability density function into a non-Gaussian and a Gaussian part. The Gaussian part is subsequently removed by modifying the characteristic function to ensure the invariance. The descriptors are based on the moments of the probability density function of the noisy random variable. The invariance property and the performance of the proposed method are demonstrated on real image data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

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

2020

Kód důvěrnosti údajů

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

Název periodika

Multidimensional Systems and Signal Processing

ISSN

1573-0824

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

1113-1143

Kód UT WoS článku

000510098800001

EID výsledku v databázi Scopus

2-s2.0-85078770837