bibtype J - Journal Article
ARLID 0638746
utime 20250909114058.1
mtime 20250909235959.9
DOI 10.1016/j.patcog.2025.112358
title (primary) (eng) Cross-Channel Blur Invariants of Color and Multispectral Images
specification
page_count 11 s.
media_type P
serial
ARLID cav_un_epca*0257388
ISSN 0031-3203
title Pattern Recognition
volume_id 172
publisher
name Elsevier
keyword Color image
keyword Blur Invariants
keyword Image Recognition
keyword Cross-Channel invariants
author (primary)
ARLID cav_un_auth*0457087
name1 Košík
name2 Václav
institution UTIA-B
full_dept (cz) Zpracování obrazové informace
full_dept (eng) Department of Image Processing
department (cz) ZOI
department (eng) ZOI
country CZ
share 50
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
share 40
garant K
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101209
name1 Šroubek
name2 Filip
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
share 10
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url https://library.utia.cas.cz/separaty/2025/ZOI/flusser-0638746.pdf
source
url https://www.sciencedirect.com/science/article/pii/S0031320325010192?via%3Dihub
cas_special
project
project_id GA24-10069S
agency GA ČR
country CZ
ARLID cav_un_auth*0472834
abstract (eng) The paper deals with the recognition of blurred color/multispectral images directly without any deblurring. We present a general theory of invariants of multispectral images with respect to blur. The paper is a significant nontrivial extension of the recent theory of blur invariants of graylevel images. The main original contribution of the paper lies in introducing cross-channel blur invariants in Fourier domain. We also developed an algorithm for their stable and fast calculation in the moment domain. Moreover, the cross-channel invariants can be found for blurs for which single-channel invariants do not exist. The experiments on simulated and real data demonstrate that incorporating the new cross-channel invariants significantly improves the recognition power and surpasses other existing approaches. The outlook for a possible implementation of the blur invariants into neural networks is briefly sketched in the conclusion.
result_subspec WOS
RIV JD
FORD0 20000
FORD1 20200
FORD2 20204
reportyear 2026
num_of_auth 3
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0369335
confidential S
article_num 112358
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mrcbU63 cav_un_epca*0257388 Pattern Recognition 172 1 2026 0031-3203 1873-5142 Elsevier