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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00200404%21RIV13-MSM-21230___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
Most image labeling problems such as segmentation and image reconstruction are fundamentally ill-posed and suffer from ambiguities and noise. Higher order image priors encode high level structural dependencies between pixels and are key to overcoming these problems. However, these priors in general lead to computationally intractable models. This paper addresses the problem of discovering compact representations of higher order priors which allow efficient inference. We propose a framework for solving this problem which uses a recently proposed representation of higher order functions where they are encoded as lower envelopes of linear functions. Maximum a Posterior inference on our learned models reduces to minimizing a pairwise function of discrete variables, which can be done approximately using standard methods. We show that our framework can learn a compact representation that approximates a prior that encourages low curvature shapes. We evaluate the approximation accuracy, discuss properties of the trained model, and demonstrate it on shape inpainting and image segmentation problems. Most image labeling problems such as segmentation and image reconstruction are fundamentally ill-posed and suffer from ambiguities and noise. Higher order image priors encode high level structural dependencies between pixels and are key to overcoming these problems. However, these priors in general lead to computationally intractable models. This paper addresses the problem of discovering compact representations of higher order priors which allow efficient inference. We propose a framework for solving this problem which uses a recently proposed representation of higher order functions where they are encoded as lower envelopes of linear functions. Maximum a Posterior inference on our learned models reduces to minimizing a pairwise function of discrete variables, which can be done approximately using standard methods. We show that our framework can learn a compact representation that approximates a prior that encourages low curvature shapes. We evaluate the approximation accuracy, discuss properties of the trained model, and demonstrate it on shape inpainting and image segmentation problems.
dcterms:title
Curvature Prior for MRF-based Segmentation and Shape Inpainting Curvature Prior for MRF-based Segmentation and Shape Inpainting
skos:prefLabel
Curvature Prior for MRF-based Segmentation and Shape Inpainting Curvature Prior for MRF-based Segmentation and Shape Inpainting
skos:notation
RIV/68407700:21230/12:00200404!RIV13-MSM-21230___
n8:predkladatel
n9:orjk%3A21230
n4:aktivita
n11:P
n4:aktivity
P(7E10044)
n4:dodaniDat
n17:2013
n4:domaciTvurceVysledku
Shekhovtsov, Oleksandr
n4:druhVysledku
n15:D
n4:duvernostUdaju
n6:S
n4:entitaPredkladatele
n20:predkladatel
n4:idSjednocenehoVysledku
129300
n4:idVysledku
RIV/68407700:21230/12:00200404
n4:jazykVysledku
n18:eng
n4:klicovaSlova
image labeling; segmentation; shape Inpainting; MFR
n4:klicoveSlovo
n16:MFR n16:shape%20Inpainting n16:segmentation n16:image%20labeling
n4:kontrolniKodProRIV
[217E5D33D003]
n4:mistoKonaniAkce
Graz
n4:mistoVydani
Heidelberg
n4:nazevZdroje
DAGM/OAGM 2012: Pattern Recognition - Joint 34th DAGM and 36th OAGM Symposium
n4:obor
n13:JD
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
3
n4:projekt
n19:7E10044
n4:rokUplatneniVysledku
n17:2012
n4:tvurceVysledku
Shekhovtsov, Oleksandr Kohli, P. Rother, C.
n4:typAkce
n5:WRD
n4:zahajeniAkce
2012-08-29+02:00
s:issn
0302-9743
s:numberOfPages
11
n12:hasPublisher
Springer-Verlag
n21:isbn
978-3-642-32716-2
n14:organizacniJednotka
21230