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 |
|
|
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 |
|
source |
|
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 |
|