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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F11%3A86080950%21RIV12-MSM-27240___
rdf:type
n10:Vysledek skos:Concept
dcterms:description
In image segmentation, measuring the distances is an important problem. The distance should tell whether two image points belong to a single or, respectively, to two different image segments. Although the Euclidean distance is often used, the disadvantage is that it does not take into account anything what happens between the points whose distance is measured. In this paper, we introduce a new quantity called the energy-transfer proximity that reflects the distances between the points on the image manifold and that can be used in the image-segmentation algorithms. In the paper, we focus especially on its use in the algorithm that is based on k-means clustering. The needed theory as well as some experimental results are presented. In image segmentation, measuring the distances is an important problem. The distance should tell whether two image points belong to a single or, respectively, to two different image segments. Although the Euclidean distance is often used, the disadvantage is that it does not take into account anything what happens between the points whose distance is measured. In this paper, we introduce a new quantity called the energy-transfer proximity that reflects the distances between the points on the image manifold and that can be used in the image-segmentation algorithms. In the paper, we focus especially on its use in the algorithm that is based on k-means clustering. The needed theory as well as some experimental results are presented.
dcterms:title
Image segmentation based on k-means clustering and energy-transfer proximity Image segmentation based on k-means clustering and energy-transfer proximity
skos:prefLabel
Image segmentation based on k-means clustering and energy-transfer proximity Image segmentation based on k-means clustering and energy-transfer proximity
skos:notation
RIV/61989100:27240/11:86080950!RIV12-MSM-27240___
n10:predkladatel
n16:orjk%3A27240
n4:aktivita
n19:S
n4:aktivity
S
n4:cisloPeriodika
6939
n4:dodaniDat
n15:2012
n4:domaciTvurceVysledku
n5:5223806 n5:4899423 n5:4442539
n4:druhVysledku
n6:J
n4:duvernostUdaju
n17:S
n4:entitaPredkladatele
n13:predkladatel
n4:idSjednocenehoVysledku
203555
n4:idVysledku
RIV/61989100:27240/11:86080950
n4:jazykVysledku
n11:eng
n4:klicovaSlova
image segmentation, k-means, proximity, diffusion equation
n4:klicoveSlovo
n9:proximity n9:k-means n9:diffusion%20equation n9:image%20segmentation
n4:kodStatuVydavatele
DE - Spolková republika Německo
n4:kontrolniKodProRIV
[F1D2A5745EA9]
n4:nazevZdroje
Lecture Notes in Computer Science
n4:obor
n8:IN
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n15:2011
n4:svazekPeriodika
2011
n4:tvurceVysledku
Krumnikl, Michal Gaura, Jan Sojka, Eduard
s:issn
0302-9743
s:numberOfPages
10
n18:doi
10.1007/978-3-642-24031-7_57
n3:organizacniJednotka
27240