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| rdf:type
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| 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. (en)
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| Title
| - Windowpane Detection based on Maximum Aposteriori Labeling
- Windowpane Detection based on Maximum Aposteriori Labeling (en)
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| skos:prefLabel
| - Windowpane Detection based on Maximum Aposteriori Labeling
- Windowpane Detection based on Maximum Aposteriori Labeling (en)
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| skos:notation
| - RIV/68407700:21230/07:00218868!RIV15-MSM-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:00218868
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Segmentation; structure model; Markov Random Fields; MRF; Potts model; labeling (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...telVyzkumneZpravy
| - Center for Machine Perception, K13133 FEE, Czech Technical University
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
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| number of pages
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| http://localhost/t...ganizacniJednotka
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