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  • This paper presents a novel approach to handle large geometric data. A data stream clustering is used to reduce the amount of data and build a hierarchy of clusters. The data stream concept allows for the processing of very large data sets. The cluster hierarchy is used in a dynamic triangulation to create a multiresolution model. It allows for the interactive selection of a different level of detail in various parts of the data. A method for removal multiple points from Delaunay triangulation is proposed. It is significantly faster than the traditional approach. The clustering and the triangulation are supplemented by an elliptical metric to handle data with anisotropic properties. Compared to the closest competitive method by Isenburg et al., the presented algorithm requires only a single pass over the data and offers a high flexibility. The method was tested on several large digital elevation maps. Once the cluster hierarchy is built, the terrains can be efficiently manipulated in real time.
  • This paper presents a novel approach to handle large geometric data. A data stream clustering is used to reduce the amount of data and build a hierarchy of clusters. The data stream concept allows for the processing of very large data sets. The cluster hierarchy is used in a dynamic triangulation to create a multiresolution model. It allows for the interactive selection of a different level of detail in various parts of the data. A method for removal multiple points from Delaunay triangulation is proposed. It is significantly faster than the traditional approach. The clustering and the triangulation are supplemented by an elliptical metric to handle data with anisotropic properties. Compared to the closest competitive method by Isenburg et al., the presented algorithm requires only a single pass over the data and offers a high flexibility. The method was tested on several large digital elevation maps. Once the cluster hierarchy is built, the terrains can be efficiently manipulated in real time. (en)
Title
  • Dynamic hierarchical traingulation of a clustered data stream
  • Dynamic hierarchical traingulation of a clustered data stream (en)
skos:prefLabel
  • Dynamic hierarchical traingulation of a clustered data stream
  • Dynamic hierarchical traingulation of a clustered data stream (en)
skos:notation
  • RIV/49777513:23520/11:43899303!RIV12-GA0-23520___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/09/0097)
http://linked.open...iv/cisloPeriodika
  • 8
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 195741
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/11:43899303
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • large data, data stream, hierarchical clustering, dynamic trianglulation, multiresolution, eliptical metric (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV
  • [FC689D6F9D0E]
http://linked.open...i/riv/nazevZdroje
  • COMPUTERS & GEOSCIENCES
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 37
http://linked.open...iv/tvurceVysledku
  • Kolingerová, Ivana
  • Skála, Jiří
issn
  • 0098-3004
number of pages
http://localhost/t...ganizacniJednotka
  • 23520
is http://linked.open...avai/riv/vysledek of
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