Blur Invariants for Image Recognition

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Blur Invariants for Image Recognition

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Blur Invariants for Image Recognition

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/GA21-03921S" target="_blank" >GA21-03921S: Inverzní problémy ve zpracování obrazu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Computer Vision

ISSN

0920-5691

e-ISSN

1573-1405

Svazek periodika

131

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

18

Strana od-do

2298-2315

Kód UT WoS článku

001000360800003

EID výsledku v databázi Scopus

2-s2.0-85160864192