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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F04%3A03103450%21RIV%2F2005%2FAV0%2F212305%2FN
rdf:type
n17:Vysledek skos:Concept
dcterms:description
Usually, object segmentation and motion estimation are considered (and modelled) as different tasks. For motion estimation this leads to problems arising especially at the boundary of an object moving in front of another if e.g. prior assumptions about continuity of the motion field are made. Thus we expect that a good segmentation will improve the motion estimation and vice versa. To demonstrate this, we consider the simple task of joint segmentation and motion estimation of an arbitrary (non-rigid) object moving in front of a still background. We propose a statistical model which represents the moving object as a triangular mesh of pairs of corresponding points and introduce an provably correct iterative scheme, which simultaneously finds the optimal segmentation and corresponding motion field. Usually, object segmentation and motion estimation are considered (and modelled) as different tasks. For motion estimation this leads to problems arising especially at the boundary of an object moving in front of another if e.g. prior assumptions about continuity of the motion field are made. Thus we expect that a good segmentation will improve the motion estimation and vice versa. To demonstrate this, we consider the simple task of joint segmentation and motion estimation of an arbitrary (non-rigid) object moving in front of a still background. We propose a statistical model which represents the moving object as a triangular mesh of pairs of corresponding points and introduce an provably correct iterative scheme, which simultaneously finds the optimal segmentation and corresponding motion field. Není k dispozici
dcterms:title
Joint Non-rigid Motion Estimation and Segmentation Joint Non-rigid Motion Estimation and Segmentation Není k dispozici
skos:prefLabel
Joint Non-rigid Motion Estimation and Segmentation Není k dispozici Joint Non-rigid Motion Estimation and Segmentation
skos:notation
RIV/68407700:21230/04:03103450!RIV/2005/AV0/212305/N
n4:strany
631 ; 638
n4:aktivita
n15:P
n4:aktivity
P(1ET101210406)
n4:dodaniDat
n12:2005
n4:domaciTvurceVysledku
n5:8930112
n4:druhVysledku
n11:D
n4:duvernostUdaju
n19:S
n4:entitaPredkladatele
n13:predkladatel
n4:idSjednocenehoVysledku
569508
n4:idVysledku
RIV/68407700:21230/04:03103450
n4:jazykVysledku
n9:eng
n4:klicovaSlova
Computer vision; Markov random fields; motion estimation; segmentation
n4:klicoveSlovo
n8:segmentation n8:motion%20estimation n8:Computer%20vision n8:Markov%20random%20fields
n4:kontrolniKodProRIV
[B931F0EDE44E]
n4:mistoKonaniAkce
Auckland
n4:mistoVydani
Heidelberg
n4:nazevZdroje
IWCIA '04: Proceedings 10th International Workshop on Combinatorial Image Analysis
n4:obor
n18:JD
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
2
n4:projekt
n7:1ET101210406
n4:rokUplatneniVysledku
n12:2004
n4:tvurceVysledku
Šára, Radim Flach, B.
n4:typAkce
n16:WRD
n4:zahajeniAkce
2004-12-01+01:00
s:issn
0302-9743
s:numberOfPages
8
n6:hasPublisher
Springer-Verlag
n20:organizacniJednotka
21230