bibtype J - Journal Article
ARLID 0524621
utime 20240103224111.4
mtime 20200601235959.9
WOS 000510098800001
SCOPUS 85078770837
DOI 10.1007/s11045-020-00702-7
title (primary) (eng) Robust Multivariate Density Estimation under Gaussian Noise
specification
page_count 30 s.
media_type P
serial
ARLID cav_un_epca*0257286
ISSN 0923-6082
title Multidimensional Systems and Signal Processing
volume_id 31
volume 3 (2020)
page_num 1113-1143
publisher
name Springer
keyword Multivariate density
keyword Gaussian additive noise
keyword Noise-robust estimation
keyword Moments
keyword Invariant characteristics
author (primary)
ARLID cav_un_auth*0336802
name1 Kostková
name2 Jitka
institution UTIA-B
full_dept (cz) Zpracování obrazové informace
full_dept (eng) Department of Image Processing
department (cz) ZOI
department (eng) ZOI
full_dept Department of Image Processing
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101087
name1 Flusser
name2 Jan
institution UTIA-B
full_dept (cz) Zpracování obrazové informace
full_dept Department of Image Processing
department (cz) ZOI
department ZOI
full_dept Department of Image Processing
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2020/ZOI/flusser-0524621.pdf
source
url https://link.springer.com/article/10.1007/s11045-020-00702-7
cas_special
project
ARLID cav_un_auth*0360229
project_id GA18-07247S
agency GA ČR
project
ARLID cav_un_auth*0392630
project_id SG18/188/OHK4/3T/14
agency GA CVUT
country CZ
abstract (eng) Observation of random variables is often corrupted by additive Gaussian noise. Noisereducing data processing is time-consuming and may introduce unwanted artifacts. In this\npaper, 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.
result_subspec WOS
RIV JD
FORD0 20000
FORD1 20200
FORD2 20204
reportyear 2021
num_of_auth 2
mrcbC52 4 A sml 4as 20231122144933.0
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0308967
confidential S
contract
name Copyright
date 20200122
mrcbC86 3+4 Article Computer Science Theory Methods|Engineering Electrical Electronic
mrcbC91 C
mrcbT16-e COMPUTERSCIENCETHEORYMETHODS|ENGINEERINGELECTRICALELECTRONIC
mrcbT16-i 0.34229
mrcbT16-j 0.41
mrcbT16-s 0.337
mrcbT16-B 33.494
mrcbT16-D Q3
mrcbT16-E Q4
arlyear 2020
mrcbTft \nSoubory v repozitáři: flusser-kostkova-0524621-copyright.pdf
mrcbU14 85078770837 SCOPUS
mrcbU24 PUBMED
mrcbU34 000510098800001 WOS
mrcbU63 cav_un_epca*0257286 Multidimensional Systems and Signal Processing 0923-6082 1573-0824 Roč. 31 č. 3 2020 1113 1143 Springer