Image segmentation based on k-means clustering and energy-transfer proximity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F11%3A86080950" target="_blank" >RIV/61989100:27240/11:86080950 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-24031-7_57" target="_blank" >http://dx.doi.org/10.1007/978-3-642-24031-7_57</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-24031-7_57" target="_blank" >10.1007/978-3-642-24031-7_57</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Image segmentation based on k-means clustering and energy-transfer proximity

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Image segmentation based on k-means clustering and energy-transfer proximity

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Svazek periodika

2011

Číslo periodika v rámci svazku

6939

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

10

Strana od-do

567-577

Kód UT WoS článku

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EID výsledku v databázi Scopus

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