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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F11%3A00187553%21RIV12-MSM-21230___
rdf:type
skos:Concept n19:Vysledek
dcterms:description
We propose a novel method for recognition of structured images and demonstrate it on detection of windows in facade images. Given an ability to obtain local low-level data evidence on primitive elements of a structure (like window in a facade image), we determine their most probable number, attribute values (location, size) and neighborhood relation. The embedded structure is weakly modeled by pair-wise attribute constraints, which allow structure and attribute constraints to mutually support each other. We use a very general framework of reversible jump MCMC, which allows simple implementation of a specific structure model and plug-in of almost arbitrary element classifiers. The MC controls the classifier by prescribing it 'where to look', without wasting too much time on unpromising locations. We have chosen the domain of window recognition in facade images to demonstrate that the result is an efficient algorithm achieving performance of other strongly informed methods for regular structures like grids, while our general model covers loosely regular configurations as well. We propose a novel method for recognition of structured images and demonstrate it on detection of windows in facade images. Given an ability to obtain local low-level data evidence on primitive elements of a structure (like window in a facade image), we determine their most probable number, attribute values (location, size) and neighborhood relation. The embedded structure is weakly modeled by pair-wise attribute constraints, which allow structure and attribute constraints to mutually support each other. We use a very general framework of reversible jump MCMC, which allows simple implementation of a specific structure model and plug-in of almost arbitrary element classifiers. The MC controls the classifier by prescribing it 'where to look', without wasting too much time on unpromising locations. We have chosen the domain of window recognition in facade images to demonstrate that the result is an efficient algorithm achieving performance of other strongly informed methods for regular structures like grids, while our general model covers loosely regular configurations as well.
dcterms:title
A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images
skos:prefLabel
A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images
skos:notation
RIV/68407700:21230/11:00187553!RIV12-MSM-21230___
n19:predkladatel
n20:orjk%3A21230
n3:aktivita
n14:Z n14:S
n3:aktivity
S, Z(MSM6840770012)
n3:dodaniDat
n6:2012
n3:domaciTvurceVysledku
n21:7065388 n21:8930112
n3:druhVysledku
n22:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
184256
n3:idVysledku
RIV/68407700:21230/11:00187553
n3:jazykVysledku
n7:eng
n3:klicovaSlova
structural recognition; stochastic inference; MCMC; facades; windows
n3:klicoveSlovo
n5:MCMC n5:stochastic%20inference n5:windows n5:facades n5:structural%20recognition
n3:kontrolniKodProRIV
[95D6A8739265]
n3:mistoKonaniAkce
Queenstown
n3:mistoVydani
Berlin
n3:nazevZdroje
ACCV 2010: Proceedings of the 10th Asian Conference on Computer Vision, Part I
n3:obor
n17:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n6:2011
n3:tvurceVysledku
Šára, Radim Tyleček, Radim
n3:typAkce
n13:WRD
n3:wos
000296690900035
n3:zahajeniAkce
2010-11-08+01:00
n3:zamer
n8:MSM6840770012
s:issn
0302-9743
s:numberOfPages
14
n11:hasPublisher
Springer-Verlag
n15:isbn
978-3-642-19314-9
n12:organizacniJednotka
21230