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  • 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. (en)
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 (en)
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  • 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 (en)
skos:notation
  • RIV/61989100:27240/10:86077663!RIV12-MPO-27240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(FR-TI1/262)
http://linked.open...vai/riv/dodaniDat
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  • 249072
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  • RIV/61989100:27240/10:86077663
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  • mean-shift algorithm; image segmentation; geodesic distance; euclidean distance (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [145D0A5CF385]
http://linked.open...v/mistoKonaniAkce
  • Las Vegas
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  • Berlín
http://linked.open...i/riv/nazevZdroje
  • Advances in visual computing : 6th international symposium, ISVC 2010 : : proceedings, parts I-III
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http://linked.open...iv/tvurceVysledku
  • Fabián, Tomáš
  • Gaura, Jan
  • Krumnikl, Michal
  • Sojka, Eduard
  • Šrubař, Štěpán
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000290358400032
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-17277-9_32
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  • Springer-Verlag
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  • 978-3-642-17276-2
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  • 27240
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