Smooth Chan-Vese Segmentation via Graph Cuts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F12%3A00057198" target="_blank" >RIV/00216224:14330/12:00057198 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0167865512000955" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0167865512000955</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.patrec.2012.03.013" target="_blank" >10.1016/j.patrec.2012.03.013</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Smooth Chan-Vese Segmentation via Graph Cuts

Popis výsledku v původním jazyce

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, coarsesegmentation 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 methodfits 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 solutio

Název v anglickém jazyce

Smooth Chan-Vese Segmentation via Graph Cuts

Popis výsledku anglicky

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, coarsesegmentation 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 methodfits 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 solutio

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Pattern Recognition Letters

ISSN

0167-8655

e-ISSN

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Svazek periodika

33

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

1405-1410

Kód UT WoS článku

000305771400018

EID výsledku v databázi Scopus

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