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• W3194831109 abstract "For the purpose of analyzing a building according to its accessibility or structural resilience, printed 2D floorplans are not sufficient because of the missing link to semantic information. This paper tackles this issue andintroduces a concept for clustering classified lines of a floor plan and for creating semantically enriched contourelements based on different image processing, computer vision and machine learning algorithms. Based on ageneral line clustering approach, we introduce type specific methods forwalls,windows,doorsandstairs. Theresulting clusters are in turn used for a contour creation, which uses minimal rotated rectangles. Those rectanglesare transformed to polygons that are refined using post processing steps. The approach is evaluated via positivetesting using a pixel-based comparison of the process’s result. For this, automatically generated as well as realworld building plans are used. The final evaluation shows, that the concept reaches a confidence of >90% for door,stair and windows and only around 10% for stairs with the run-time linearly scaling with the size of the input." @default.
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• W3194831109 date "2021-01-01" @default.
• W3194831109 modified "2023-09-25" @default.
• W3194831109 title "Line Clustering and Contour Extraction in the Context of 2D Building Plans" @default.
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• W3194831109 doi "https://doi.org/10.24132/csrn.2021.3101.2" @default.
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