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  • Není k dispozici (cs)
  • A new robust matching method is proposed. The Progressive Sample Consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative correspondences. Unlike RANSAC, which treats all correspondences equally and draws random samples uniformly from the full set, PROSAC samples are drawn from progressively larger sets of top-ranked correspondences. Under the mild assumption that the similarity measure predicts correctness of a match better than random guessing, we show that PROSAC achieves large computational savings. Experiments demonstrate it is often significantly faster (up to more than hundred times) than RANSAC. For the derived size of the sampled set of correspondences as a function of the number of samples already drawn, PROSAC converges towards RANSAC in the worst case. The power of the method is demonstrated on widebaseline matching problems.
  • A new robust matching method is proposed. The Progressive Sample Consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative correspondences. Unlike RANSAC, which treats all correspondences equally and draws random samples uniformly from the full set, PROSAC samples are drawn from progressively larger sets of top-ranked correspondences. Under the mild assumption that the similarity measure predicts correctness of a match better than random guessing, we show that PROSAC achieves large computational savings. Experiments demonstrate it is often significantly faster (up to more than hundred times) than RANSAC. For the derived size of the sampled set of correspondences as a function of the number of samples already drawn, PROSAC converges towards RANSAC in the worst case. The power of the method is demonstrated on widebaseline matching problems. (en)
Title
  • Matching with PROSAC - Progressive Sample Consensus
  • Není k dispozici (cs)
  • Matching with PROSAC - Progressive Sample Consensus (en)
skos:prefLabel
  • Matching with PROSAC - Progressive Sample Consensus
  • Není k dispozici (cs)
  • Matching with PROSAC - Progressive Sample Consensus (en)
skos:notation
  • RIV/68407700:21230/05:03109919!RIV06-GA0-21230___
http://linked.open.../vavai/riv/strany
  • 220 ; 226
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/03/0440)
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
  • 528941
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/05:03109919
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • RANSAC; wide-baseline stereo (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [67398F32CC0D]
http://linked.open...v/mistoKonaniAkce
  • San Diego
http://linked.open...i/riv/mistoVydani
  • Los Alamitos
http://linked.open...i/riv/nazevZdroje
  • Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR)
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...iv/tvurceVysledku
  • Matas, Jiří
  • Chum, Ondřej
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society Press
https://schema.org/isbn
  • 0-7695-2372-2
http://localhost/t...ganizacniJednotka
  • 21230
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