Hierarchical Blurring Mean-Shift

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F11%3A86081117" target="_blank" >RIV/61989100:27240/11:86081117 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-23687-7_21" target="_blank" >http://dx.doi.org/10.1007/978-3-642-23687-7_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-23687-7_21" target="_blank" >10.1007/978-3-642-23687-7_21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Blurring Mean-Shift

Popis výsledku v původním jazyce

In recent years, various Mean-Shift methods were used for filtration and segmentation of images and other datasets. These methods achieve good segmentation results, but the computational speed is sometimes very low, especially for big images and some specific settings. In this paper, we propose an improved segmentation method that we call Hierarchical Blurring Mean-Shift. The method achieve significant reduction of computation time and minimal influence on segmentation quality. A comparison of our method with traditional Blurring Mean-Shift and Hierarchical Mean-Shift with respect to the quality of segmentation and computational time is demonstrated. Furthermore, we study the influence of parameter settings in various hierarchy depths on computationaltime and number of segments. Finally, the results promising reliable and fast image segmentation are presented.

Název v anglickém jazyce

Hierarchical Blurring Mean-Shift

Popis výsledku anglicky

In recent years, various Mean-Shift methods were used for filtration and segmentation of images and other datasets. These methods achieve good segmentation results, but the computational speed is sometimes very low, especially for big images and some specific settings. In this paper, we propose an improved segmentation method that we call Hierarchical Blurring Mean-Shift. The method achieve significant reduction of computation time and minimal influence on segmentation quality. A comparison of our method with traditional Blurring Mean-Shift and Hierarchical Mean-Shift with respect to the quality of segmentation and computational time is demonstrated. Furthermore, we study the influence of parameter settings in various hierarchy depths on computationaltime and number of segments. Finally, the results promising reliable and fast image segmentation 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

—

Svazek periodika

2011

Číslo periodika v rámci svazku

6915

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

11

Strana od-do

228-238

Kód UT WoS článku

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EID výsledku v databázi Scopus

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