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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F10%3A00175492%21RIV11-MSM-21230___
rdf:type
n20:Vysledek skos:Concept
dcterms:description
We propose a new robust focal length estimation method in multi-view structure from motion from unordered data sets, e.g. downloaded from the Flickr database, where jpeg-exif headers are often incorrect or missing. The method is based on a combination of RANSAC with weighted kernel voting and can use any algorithm for estimating epipolar geometry and unknown focal lengths. We demonstrate by experiments with synthetic and real data that the method produces reliable focal length estimates which are better than estimates obtained using RANSAC or kernel voting alone and which are in most real situations very close to the ground truth. An important feature of this method is the ability to detect image pairs close to critical configurations or the cases when the focal length can't be reliably estimated. We propose a new robust focal length estimation method in multi-view structure from motion from unordered data sets, e.g. downloaded from the Flickr database, where jpeg-exif headers are often incorrect or missing. The method is based on a combination of RANSAC with weighted kernel voting and can use any algorithm for estimating epipolar geometry and unknown focal lengths. We demonstrate by experiments with synthetic and real data that the method produces reliable focal length estimates which are better than estimates obtained using RANSAC or kernel voting alone and which are in most real situations very close to the ground truth. An important feature of this method is the ability to detect image pairs close to critical configurations or the cases when the focal length can't be reliably estimated.
dcterms:title
Robust focal length estimation by voting in multiview scene reconstruction Robust focal length estimation by voting in multiview scene reconstruction
skos:prefLabel
Robust focal length estimation by voting in multiview scene reconstruction Robust focal length estimation by voting in multiview scene reconstruction
skos:notation
RIV/68407700:21230/10:00175492!RIV11-MSM-21230___
n3:aktivita
n10:Z n10:R n10:P
n3:aktivity
P(7E09062), R, Z(MSM6840770038)
n3:dodaniDat
n9:2011
n3:domaciTvurceVysledku
n8:6245269 n8:6479243 n8:6349390
n3:druhVysledku
n17:D
n3:duvernostUdaju
n6:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
285573
n3:idVysledku
RIV/68407700:21230/10:00175492
n3:jazykVysledku
n16:eng
n3:klicovaSlova
focal length; epipolar geometry; 3D reconstruction
n3:klicoveSlovo
n11:3D%20reconstruction n11:focal%20length n11:epipolar%20geometry
n3:kontrolniKodProRIV
[95C5323F18B9]
n3:mistoKonaniAkce
Xian
n3:mistoVydani
Heidelberg
n3:nazevZdroje
ACCV 2009: Proceedings of the 9th Asian Conference on Computer Vision, Part I
n3:obor
n7:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n4:7E09062
n3:rokUplatneniVysledku
n9:2010
n3:tvurceVysledku
Bujňák, Martin Kúkelová, Zuzana Pajdla, Tomáš
n3:typAkce
n13:WRD
n3:wos
000279642500002
n3:zahajeniAkce
2009-09-23+02:00
n3:zamer
n19:MSM6840770038
s:issn
0302-9743
s:numberOfPages
12
n12:hasPublisher
Springer-Verlag
n21:isbn
978-3-642-12306-1
n22:organizacniJednotka
21230