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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F12%3A00057198%21RIV13-GA0-14330___
rdf:type
n12:Vysledek skos:Concept
rdfs:seeAlso
http://www.sciencedirect.com/science/article/pii/S0167865512000955
dcterms:description
The graph cut framework presents an efficient method for approximating the minimum of the popular Chan-Vese functional for image segmentation. However, a fundamental drawback of graph cuts is a need for a dense neighbourhood system in order to avoid geometric artefacts and jagged boundaries. The increasing connectivity leads to excessive memory consumption and burdens the efficiency of the method. In this paper, we address the issue by introducing a two-stage connectivity scaling approach. First, coarse segmentation is calculated using a sparse neighbourhood over the whole image. In the second stage, the segmentation is refined by employing a dense neighbourhood in a narrow band around the boundary from the first stage. We demonstrate that this method fits well with the Chan-Vese functional and yields smooth boundaries without increasing the computational demands significantly. Moreover, under specific conditions, the construction has no negative effect on the optimality of the solution. The graph cut framework presents an efficient method for approximating the minimum of the popular Chan-Vese functional for image segmentation. However, a fundamental drawback of graph cuts is a need for a dense neighbourhood system in order to avoid geometric artefacts and jagged boundaries. The increasing connectivity leads to excessive memory consumption and burdens the efficiency of the method. In this paper, we address the issue by introducing a two-stage connectivity scaling approach. First, coarse segmentation is calculated using a sparse neighbourhood over the whole image. In the second stage, the segmentation is refined by employing a dense neighbourhood in a narrow band around the boundary from the first stage. We demonstrate that this method fits well with the Chan-Vese functional and yields smooth boundaries without increasing the computational demands significantly. Moreover, under specific conditions, the construction has no negative effect on the optimality of the solution.
dcterms:title
Smooth Chan-Vese Segmentation via Graph Cuts Smooth Chan-Vese Segmentation via Graph Cuts
skos:prefLabel
Smooth Chan-Vese Segmentation via Graph Cuts Smooth Chan-Vese Segmentation via Graph Cuts
skos:notation
RIV/00216224:14330/12:00057198!RIV13-GA0-14330___
n12:predkladatel
n13:orjk%3A14330
n3:aktivita
n8:Z n8:S n8:P
n3:aktivity
P(2B06052), P(GBP302/12/G157), P(LC535), S, Z(MSM0021622419)
n3:cisloPeriodika
10
n3:dodaniDat
n5:2013
n3:domaciTvurceVysledku
n4:8900736 n4:3021475 n4:9465146
n3:druhVysledku
n17:J
n3:duvernostUdaju
n6:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
168640
n3:idVysledku
RIV/00216224:14330/12:00057198
n3:jazykVysledku
n18:eng
n3:klicovaSlova
image segmentation; graph cut framework; Chan-Vese model; boundary smoothness; memory consumption
n3:klicoveSlovo
n11:graph%20cut%20framework n11:Chan-Vese%20model n11:image%20segmentation n11:memory%20consumption n11:boundary%20smoothness
n3:kodStatuVydavatele
NL - Nizozemsko
n3:kontrolniKodProRIV
[880E605881AE]
n3:nazevZdroje
Pattern Recognition Letters
n3:obor
n9:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n15:LC535 n15:2B06052 n15:GBP302%2F12%2FG157
n3:rokUplatneniVysledku
n5:2012
n3:svazekPeriodika
33
n3:tvurceVysledku
Kozubek, Michal Maška, Martin Daněk, Ondřej Matula, Pavel
n3:wos
000305771400018
n3:zamer
n20:MSM0021622419
s:issn
0167-8655
s:numberOfPages
6
n19:doi
10.1016/j.patrec.2012.03.013
n21:organizacniJednotka
14330