Blurring mean-shift with a restricted data-set modification for applications in image processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F10%3A86077663" target="_blank" >RIV/61989100:27240/10:86077663 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-17277-9_32" target="_blank" >http://dx.doi.org/10.1007/978-3-642-17277-9_32</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-17277-9_32" target="_blank" >10.1007/978-3-642-17277-9_32</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Blurring mean-shift with a restricted data-set modification for applications in image processing

Popis výsledku v původním jazyce

A new mean-shift technique, blurring mean-shift with a restricted dataset modification, is presented. It is mainly intended for applications in image processing since, in this case, the coordinates of the points entering into the mean-shift procedure maybe obviously split into two parts that are treated in different ways: The spatial part (geometrical position in image) and the range part (colour/brightness). The basic principle is similar as in the blurring mean-shift algorithm. In contrast to it, thechanges of the dataset are restricted only to the range values (colour/brightness); the spatial parts do not change. The points that are processed during computation may be viewed as points of a certain image that evolves during the iterations. We showthat the process converges. As a result, an image is obtained with the areas of constant colour/brightness, which can be exploited for image filtering and segmentation. The results of testing are presented showing that the algorithm is us

Název v anglickém jazyce

Blurring mean-shift with a restricted data-set modification for applications in image processing

Popis výsledku anglicky

A new mean-shift technique, blurring mean-shift with a restricted dataset modification, is presented. It is mainly intended for applications in image processing since, in this case, the coordinates of the points entering into the mean-shift procedure maybe obviously split into two parts that are treated in different ways: The spatial part (geometrical position in image) and the range part (colour/brightness). The basic principle is similar as in the blurring mean-shift algorithm. In contrast to it, thechanges of the dataset are restricted only to the range values (colour/brightness); the spatial parts do not change. The points that are processed during computation may be viewed as points of a certain image that evolves during the iterations. We showthat the process converges. As a result, an image is obtained with the areas of constant colour/brightness, which can be exploited for image filtering and segmentation. The results of testing are presented showing that the algorithm is us

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/FR-TI1%2F262" target="_blank" >FR-TI1/262: *Výzkum a vývoj zařízení pro nesmazatelné značení dlouhých kovových výrobků</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in visual computing : 6th international symposium, ISVC 2010 : : proceedings, parts I-III

ISBN

978-3-642-17276-2

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

10

Strana od-do

310-319

Název nakladatele

Springer Verlag

Místo vydání

Berlín

Místo konání akce

Las Vegas

Datum konání akce

29. 11. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000290358400032