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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F05%3A03109919%21RIV06-GA0-21230___
rdf:type
n12:Vysledek skos:Concept
dcterms:description
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. Není k dispozici
dcterms:title
Matching with PROSAC - Progressive Sample Consensus Matching with PROSAC - Progressive Sample Consensus Není k dispozici
skos:prefLabel
Matching with PROSAC - Progressive Sample Consensus Matching with PROSAC - Progressive Sample Consensus Není k dispozici
skos:notation
RIV/68407700:21230/05:03109919!RIV06-GA0-21230___
n3:strany
220 ; 226
n3:aktivita
n5:P
n3:aktivity
P(GA102/03/0440)
n3:dodaniDat
n4:2006
n3:domaciTvurceVysledku
n21:7004818 n21:1711326
n3:druhVysledku
n13:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
528941
n3:idVysledku
RIV/68407700:21230/05:03109919
n3:jazykVysledku
n9:eng
n3:klicovaSlova
RANSAC; wide-baseline stereo
n3:klicoveSlovo
n10:RANSAC n10:wide-baseline%20stereo
n3:kontrolniKodProRIV
[67398F32CC0D]
n3:mistoKonaniAkce
San Diego
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR)
n3:obor
n7:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n19:GA102%2F03%2F0440
n3:rokUplatneniVysledku
n4:2005
n3:tvurceVysledku
Chum, Ondřej Matas, Jiří
n3:typAkce
n18:WRD
n3:zahajeniAkce
2005-06-20+02:00
s:numberOfPages
7
n14:hasPublisher
IEEE Computer Society Press
n20:isbn
0-7695-2372-2
n8:organizacniJednotka
21230