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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. (en)
Title
  • Image segmentation based on k-means clustering and energy-transfer proximity
  • Image segmentation based on k-means clustering and energy-transfer proximity (en)
skos:prefLabel
  • Image segmentation based on k-means clustering and energy-transfer proximity
  • Image segmentation based on k-means clustering and energy-transfer proximity (en)
skos:notation
  • RIV/61989100:27240/11:86080950!RIV12-MSM-27240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...iv/cisloPeriodika
  • 6939
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 203555
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27240/11:86080950
http://linked.open...riv/jazykVysledku
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  • image segmentation, k-means, proximity, diffusion equation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • DE - Spolková republika Německo
http://linked.open...ontrolniKodProRIV
  • [F1D2A5745EA9]
http://linked.open...i/riv/nazevZdroje
  • Lecture Notes in Computer Science
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 2011
http://linked.open...iv/tvurceVysledku
  • Gaura, Jan
  • Krumnikl, Michal
  • Sojka, Eduard
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-24031-7_57
http://localhost/t...ganizacniJednotka
  • 27240
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