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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F10%3APU87043%21RIV11-MSM-26220___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports. Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports.
dcterms:title
Significant Parameters of Image Reconstruction Convergence Significant Parameters of Image Reconstruction Convergence
skos:prefLabel
Significant Parameters of Image Reconstruction Convergence Significant Parameters of Image Reconstruction Convergence
skos:notation
RIV/00216305:26220/10:PU87043!RIV11-MSM-26220___
n3:aktivita
n16:Z
n3:aktivity
Z(MSM0021630503)
n3:dodaniDat
n15:2011
n3:domaciTvurceVysledku
n5:3152383 Ostanina, Ksenia
n3:druhVysledku
n20:D
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
287268
n3:idVysledku
RIV/00216305:26220/10:PU87043
n3:jazykVysledku
n21:eng
n3:klicovaSlova
image processing, EIT
n3:klicoveSlovo
n11:image%20processing n11:EIT
n3:kontrolniKodProRIV
[409DE35888FB]
n3:mistoKonaniAkce
Žilina
n3:mistoVydani
Neuveden
n3:nazevZdroje
ELEKTRO 2010 proceedings
n3:obor
n9:JA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n15:2010
n3:tvurceVysledku
Ostanina, Ksenia Dědková, Jarmila
n3:typAkce
n18:WRD
n3:zahajeniAkce
2010-05-24+02:00
n3:zamer
n17:MSM0021630503
s:numberOfPages
4
n4:hasPublisher
Neuveden
n13:isbn
978-80-554-0196-6
n8:organizacniJednotka
26220