Robust Multivariate Density Estimation under Gaussian Noise
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Robust Multivariate Density Estimation under Gaussian Noise
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
<a href="/en/project/GA18-07247S" target="_blank" >GA18-07247S: Methods and Algorithms for Vector and Tensor Field Image Analysis</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Multidimensional Systems and Signal Processing
ISSN
1573-0824
e-ISSN
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Volume of the periodical
31
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
30
Pages from-to
1113-1143
UT code for WoS article
000510098800001
EID of the result in the Scopus database
2-s2.0-85078770837