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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A03150829%21RIV09-AV0-21230___
rdf:type
n16:Vysledek skos:Concept
dcterms:description
Helmholtz stereopsis is a relatively recent reconstruction technique which is able to reconstruct scenes with arbitrary and unknown surface reflectance properties. Conventional implementations of the method estimate surface normal direction at each surface point via an eigenanalysis, thereby optimising an algebraic distance. We develop a more physically meaningful radiometric distance whose minimisation is shown to yield a Maximum Likelihood surface normal estimate. The proposed method produces more accurate results than algebraic methods on synthetic imagery and yields excellent reconstruction results on real data. Our analysis explains why, for some imaging configurations, a sub-optimal algebraic distance can yield good results. Helmholtz stereopsis is a relatively recent reconstruction technique which is able to reconstruct scenes with arbitrary and unknown surface reflectance properties. Conventional implementations of the method estimate surface normal direction at each surface point via an eigenanalysis, thereby optimising an algebraic distance. We develop a more physically meaningful radiometric distance whose minimisation is shown to yield a Maximum Likelihood surface normal estimate. The proposed method produces more accurate results than algebraic methods on synthetic imagery and yields excellent reconstruction results on real data. Our analysis explains why, for some imaging configurations, a sub-optimal algebraic distance can yield good results. Helmholtz stereopsis is a relatively recent reconstruction technique which is able to reconstruct scenes with arbitrary and unknown surface reflectance properties. Conventional implementations of the method estimate surface normal direction at each surface point via an eigenanalysis, thereby optimising an algebraic distance. We develop a more physically meaningful radiometric distance whose minimisation is shown to yield a Maximum Likelihood surface normal estimate. The proposed method produces more accurate results than algebraic methods on synthetic imagery and yields excellent reconstruction results on real data. Our analysis explains why, for some imaging configurations, a sub-optimal algebraic distance can yield good results.
dcterms:title
A Maximum Likelihood Surface Normal Estimation Algorithm for Helmholtz Stereopsis A Maximum Likelihood Surface Normal Estimation Algorithm for Helmholtz Stereopsis A Maximum Likelihood Surface Normal Estimation Algorithm for Helmholtz Stereopsis
skos:prefLabel
A Maximum Likelihood Surface Normal Estimation Algorithm for Helmholtz Stereopsis A Maximum Likelihood Surface Normal Estimation Algorithm for Helmholtz Stereopsis A Maximum Likelihood Surface Normal Estimation Algorithm for Helmholtz Stereopsis
skos:notation
RIV/68407700:21230/08:03150829!RIV09-AV0-21230___
n3:aktivita
n7:P
n3:aktivity
P(1ET101210406)
n3:dodaniDat
n9:2009
n3:domaciTvurceVysledku
n12:9235256 n12:8930112
n3:druhVysledku
n14:D
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
354237
n3:idVysledku
RIV/68407700:21230/08:03150829
n3:jazykVysledku
n10:eng
n3:klicovaSlova
Helmholtz stereopsis; computer vision
n3:klicoveSlovo
n5:Helmholtz%20stereopsis n5:computer%20vision
n3:kontrolniKodProRIV
[9F2D4FDDDCF5]
n3:mistoKonaniAkce
Funchal, Madeira
n3:mistoVydani
Setúbal
n3:nazevZdroje
VISAPP 2008: Proceedings of the Third International Conference on Computer Vision Theory and Applications
n3:obor
n20:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:projekt
n6:1ET101210406
n3:rokUplatneniVysledku
n9:2008
n3:tvurceVysledku
Guillemaut, J. Šára, Radim Illingworth, J. Drbohlav, Ondřej
n3:typAkce
n8:WRD
n3:wos
000256791600059
n3:zahajeniAkce
2008-01-22+01:00
s:numberOfPages
8
n21:hasPublisher
INSTICC Press
n13:isbn
978-989-8111-21-0
n19:organizacniJednotka
21230