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Abstract
For the purpose of analyzing a building according to its accessibility or structural resilience, printed 2D floor plans are not sufficient because of the missing link to semantic information.
This paper tackles this issue and introduces a concept for clustering classified lines of a floor plan and for creating semantically enriched contour elements based on different image processing, computer vision and machine learning algorithms. Based on a general line clustering approach, we introduce type specific methods for walls, windows, doors and stairs. The resulting clusters are in turn used for a contour creation, which uses minimal rotated rectangles. Those rectangles are transformed to polygons that are refined using post processing steps.
The approach is evaluated via positive testing using a pixel-based comparison of the process's result. For this, automatically generated as well as real world 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.
This paper tackles this issue and introduces a concept for clustering classified lines of a floor plan and for creating semantically enriched contour elements based on different image processing, computer vision and machine learning algorithms. Based on a general line clustering approach, we introduce type specific methods for walls, windows, doors and stairs. The resulting clusters are in turn used for a contour creation, which uses minimal rotated rectangles. Those rectangles are transformed to polygons that are refined using post processing steps.
The approach is evaluated via positive testing using a pixel-based comparison of the process's result. For this, automatically generated as well as real world 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.
Originalsprache | Englisch |
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Titel | 29th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2021 - Proceedings |
Redakteure/-innen | Vaclav Skala |
Seiten | 11-20 |
Seitenumfang | 10 |
Band | 3101 |
ISBN (elektronisch) | 9788086943343 |
DOIs | |
Publikationsstatus | Veröffentlicht - Mai 2021 |
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PASS - PASS - Plan Analysis using Self-learning Solutions
Krauss, O. (Leitende(r) Forscher/-in), Zwettler, G. A. (Weitere Forschende), Pointner, A. M. (Weitere Forschende) & Praschl, C. (Weitere Forschende)
01.12.2017 → 30.11.2019
Projekt: Forschungsprojekt