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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F07%3A00218868%21RIV15-MSM-21230___
rdf:type
skos:Concept n17:Vysledek
dcterms:description
Segmentation of windowpanes in the images of facades is formulated as a task of maximum aposteriori labeling. Assuming orthographic rectification of the building facade, the windowpanes are always axis-parallel rectangles of relatively low variability in appearance. Every image pixel has one of 10 possible labels, and the adjacent pixels are interconnected via links which defines allowed label configuration, such that the labels are forced to form 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 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 does not model the structure at all, they typically only have a penalty when adjacent labels are different, in order to make resulting patches more continuous to reduce influence of noise and prevent over-segmentation. Segmentation of windowpanes in the images of facades is formulated as a task of maximum aposteriori labeling. Assuming orthographic rectification of the building facade, the windowpanes are always axis-parallel rectangles of relatively low variability in appearance. Every image pixel has one of 10 possible labels, and the adjacent pixels are interconnected via links which defines allowed label configuration, such that the labels are forced to form 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 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 does not model the structure at all, they typically only have a penalty when adjacent labels are different, in order to make resulting patches more continuous to reduce influence of noise and prevent over-segmentation.
dcterms:title
Windowpane Detection based on Maximum Aposteriori Labeling Windowpane Detection based on Maximum Aposteriori Labeling
skos:prefLabel
Windowpane Detection based on Maximum Aposteriori Labeling Windowpane Detection based on Maximum Aposteriori Labeling
skos:notation
RIV/68407700:21230/07:00218868!RIV15-MSM-21230___
n3:aktivita
n12:R
n3:aktivity
R
n3:dodaniDat
n9:2015
n3:domaciTvurceVysledku
n11:9680411 n11:8930112
n3:druhVysledku
n13:V%2FS
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
461317
n3:idVysledku
RIV/68407700:21230/07:00218868
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Segmentation; structure model; Markov Random Fields; MRF; Potts model; labeling
n3:klicoveSlovo
n6:Segmentation n6:MRF n6:labeling n6:structure%20model n6:Potts%20model n6:Markov%20Random%20Fields
n3:kontrolniKodProRIV
[CD337CE5EDF8]
n3:mistoVydani
Praha
n3:objednatelVyzkumneZpravy
Center for Machine Perception, K13133 FEE, Czech Technical University
n3:obor
n8:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n9:2007
n3:tvurceVysledku
Čech, Jan Šára, Radim
s:numberOfPages
12
n4:organizacniJednotka
21230