Blur Invariants for Image Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00573978" target="_blank" >RIV/67985556:_____/23:00573978 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11263-023-01798-7" target="_blank" >https://link.springer.com/article/10.1007/s11263-023-01798-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11263-023-01798-7" target="_blank" >10.1007/s11263-023-01798-7</a>
Alternative languages
Result language
angličtina
Original language name
Blur Invariants for Image Recognition
Original language description
Blur is an image degradation that makes object recognition challenging. Restoration approaches solve this problem via image deblurring, deep learning methods rely on the augmentation of training sets. Invariants with respect to blur offer an alternative way of describing and recognising blurred images without any deblurring and data augmentation. In this paper, we present an original theory of blur invariants. Unlike all previous attempts, the new theory requires no prior knowledge of the blur type. The invariants are constructed in the Fourier domain by means of orthogonal projection operators and moment expansion is used for efficient and stable computation. Applying a general substitution rule, combined invariants to blur and spatial transformations are easy to construct and use. Experimental comparison to Convolutional Neural Networks shows the advantages of the proposed theory.
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/GA21-03921S" target="_blank" >GA21-03921S: Inverse problems in image processing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Computer Vision
ISSN
0920-5691
e-ISSN
1573-1405
Volume of the periodical
131
Issue of the periodical within the volume
9
Country of publishing house
DE - GERMANY
Number of pages
18
Pages from-to
2298-2315
UT code for WoS article
001000360800003
EID of the result in the Scopus database
2-s2.0-85160864192