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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A00144353%21RIV10-MSM-21230___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
Segmentation of windowpanes in images of building facades is formulated as a task of maximum aposteriori probability labeling. Assuming orthographic rectification of the image, the windowpanes are always axis-parallel rectangles of relatively low variability in appearance. Every image pixel has one of 10 possible labels, and the labels in adjacent pixels are constrained by allowed label configuration, such that the image labels represent a set of non-overlapping rectangles. The task of finding the most probable labeling of a given image leads to NP-hard discrete optimization problem. However, we find an approximate solution using a general solver suitable for such problems and we obtain promising results which we demonstrate on several experiments. Substantial difference between the presented paper and the state-of-the-art papers on segmentation based on Markov Random Fields is that we have a strong structure model, forcing the labels to form rectangles, while other methods do not mode Segmentation of windowpanes in images of building facades is formulated as a task of maximum aposteriori probability labeling. Assuming orthographic rectification of the image, the windowpanes are always axis-parallel rectangles of relatively low variability in appearance. Every image pixel has one of 10 possible labels, and the labels in adjacent pixels are constrained by allowed label configuration, such that the image labels represent a set of non-overlapping rectangles. The task of finding the most probable labeling of a given image leads to NP-hard discrete optimization problem. However, we find an approximate solution using a general solver suitable for such problems and we obtain promising results which we demonstrate on several experiments. Substantial difference between the presented paper and the state-of-the-art papers on segmentation based on Markov Random Fields is that we have a strong structure model, forcing the labels to form rectangles, while other methods do not mode
dcterms:title
Windowpane Detection based on Maximum Aposteriori Probability Labeling Windowpane Detection based on Maximum Aposteriori Probability Labeling
skos:prefLabel
Windowpane Detection based on Maximum Aposteriori Probability Labeling Windowpane Detection based on Maximum Aposteriori Probability Labeling
skos:notation
RIV/68407700:21230/08:00144353!RIV10-MSM-21230___
n3:aktivita
n20:R n20:P
n3:aktivity
P(1ET101210407), R
n3:dodaniDat
n21:2010
n3:domaciTvurceVysledku
n7:8930112 n7:9680411
n3:druhVysledku
n18:D
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
405795
n3:idVysledku
RIV/68407700:21230/08:00144353
n3:jazykVysledku
n12:eng
n3:klicovaSlova
Markov Random Field; constrained segmentation; probabilistic labeling; structure model
n3:klicoveSlovo
n8:Markov%20Random%20Field n8:probabilistic%20labeling n8:constrained%20segmentation n8:structure%20model
n3:kontrolniKodProRIV
[332DFA87D938]
n3:mistoKonaniAkce
Buffalo
n3:mistoVydani
Singapore
n3:nazevZdroje
Image Analysis - From Theory to Applications, Proceedings of the 12th International Workshop on Combinatorial Image Analysis (IWCIA'08)
n3:obor
n10:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n13:1ET101210407
n3:rokUplatneniVysledku
n21:2008
n3:tvurceVysledku
Šára, Radim Čech, Jan
n3:typAkce
n16:WRD
n3:zahajeniAkce
2008-04-07+02:00
s:numberOfPages
9
n19:hasPublisher
Research Publishing Services
n9:isbn
978-3-540-78274-2
n17:organizacniJednotka
21230