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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F10%3A86077663%21RIV12-MPO-27240___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
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 may be 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, the changes 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 show that 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 useful. 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 may be 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, the changes 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 show that 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 useful.
dcterms:title
Blurring mean-shift with a restricted data-set modification for applications in image processing Blurring mean-shift with a restricted data-set modification for applications in image processing
skos:prefLabel
Blurring mean-shift with a restricted data-set modification for applications in image processing Blurring mean-shift with a restricted data-set modification for applications in image processing
skos:notation
RIV/61989100:27240/10:86077663!RIV12-MPO-27240___
n3:aktivita
n19:P
n3:aktivity
P(FR-TI1/262)
n3:dodaniDat
n7:2012
n3:domaciTvurceVysledku
n8:4442539 n8:3274993 n8:5223806 n8:4899423 n8:7748078
n3:druhVysledku
n20:D
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
249072
n3:idVysledku
RIV/61989100:27240/10:86077663
n3:jazykVysledku
n21:eng
n3:klicovaSlova
mean-shift algorithm; image segmentation; geodesic distance; euclidean distance
n3:klicoveSlovo
n5:mean-shift%20algorithm n5:euclidean%20distance n5:image%20segmentation n5:geodesic%20distance
n3:kontrolniKodProRIV
[145D0A5CF385]
n3:mistoKonaniAkce
Las Vegas
n3:mistoVydani
Berlín
n3:nazevZdroje
Advances in visual computing : 6th international symposium, ISVC 2010 : : proceedings, parts I-III
n3:obor
n22:IN
n3:pocetDomacichTvurcuVysledku
5
n3:pocetTvurcuVysledku
5
n3:projekt
n10:FR-TI1%2F262
n3:rokUplatneniVysledku
n7:2010
n3:tvurceVysledku
Fabián, Tomáš Krumnikl, Michal Gaura, Jan Sojka, Eduard Šrubař, Štěpán
n3:typAkce
n9:WRD
n3:wos
000290358400032
n3:zahajeniAkce
2010-11-29+01:00
s:issn
0302-9743
s:numberOfPages
10
n11:doi
10.1007/978-3-642-17277-9_32
n13:hasPublisher
Springer-Verlag
n6:isbn
978-3-642-17276-2
n17:organizacniJednotka
27240