Statistical segmentation and structural recognition for floor plan interpretation

Notation invariant structural element recognition
  • Lluís-Pere de las Heras
  • Sheraz Ahmed
  • Marcus Liwicki
  • Ernest Valveny
  • Gemma Sánchez
Original Paper

Abstract

A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.

References

  1. 1.
    Ah-soon, C., Tombre, K.: Variations on the analysis of architectural drawings. In: Proceedings of Fourth International Conference on Document Analysis and Recognition, pp. 347–351 (1997)Google Scholar
  2. 2.
    Ahmed, S., Liwicki, M., Weber, M., Dengel, A.: Improved automatic analysis of architectural floor plans. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 864–869 (2011)Google Scholar
  3. 3.
    Ahmed, S., Liwicki, M., Weber, M., Dengel., A.: Automatic room detection and room labeling from architectural floor plans. In: Proceedings of the IAPR International Workshop on Document Analysis Systems, pp. 339–343. IEEE (2012)Google Scholar
  4. 4.
    Ahmed, S., Weber, M., Liwicki, M., Langenhan, C., Dengel, A., Petzold, F.: Automatic analysis and sketch-based retrieval of architectural floor plans. Pattern Recognition Letters (pre-print) (2013)Google Scholar
  5. 5.
    Aoki, Y., Shio, A., Arai, H., Odaka, K.: A prototype system for interpreting hand-sketched floor plans. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 3, pp. 747–751 (1996)Google Scholar
  6. 6.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: Proceedings of the European Conference on Computer Vision, pp. 404–417 (2006)Google Scholar
  7. 7.
    Boumaiza, A., Tabbone, S.: Impact of a codebook filtering step on a galois lattice structure for graphics recognition. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 278–281 (2012)Google Scholar
  8. 8.
    Cherneff, J., Logcher, R., Connor, J., Patrikalakis, N.: Knowledge-based interpretation of architectural drawings. Res. Eng. Des. 3, 195–210 (1992)CrossRefGoogle Scholar
  9. 9.
    Dosch, P., Masini, G.: Reconstruction of the 3d structure of a building from the 2d drawings of its floors. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 487–490 (1999)Google Scholar
  10. 10.
    Dosch, P., Tombre, K., Ah-Soon, C., Masini, G.: A complete system for the analysis of architectural drawings. Int. J. Doc. Anal. Recogn. 3, 102–116 (2000)CrossRefGoogle Scholar
  11. 11.
    Dutta, A., Lladós, J., Pal, U.: Symbol spotting in line drawings through graph paths hashing. In: Proceedings of the 11th International Conference on Document Analysis and Recognition, pp. 982–986 (2011)Google Scholar
  12. 12.
    de las Heras, L.P., Fernández, D., Valveny, E., Lladós, J., Sánchez, G.: Unsupervised wall detector in architectural floorplans. In: Proceedings of the 12th International Conference on Document Analysis and Recognition, pp. 1277–1281 (2013)Google Scholar
  13. 13.
    de las Heras, L.P., Mas, J., Sánchez, G., Valveny, E.: Wall patch-based segmentation in architectural floorplans. In: Proceedings of the 11th International Conference on Document Analysis and Recognition, pp. 1270–1274 (2011)Google Scholar
  14. 14.
    de las Heras, L.P., Mas, J., Sánchez, G., Valveny, E.: Notation-invariant patch-based wall detector in architectural floor plans. In: Graphic Recognition, Lecture Notes in Computer Science, vol. 7423, pp. 79–88 (2012)Google Scholar
  15. 15.
    de las Heras, L.P., Sánchez, G.: And-or graph grammar for architectural floorplan representation, learning and recognition. a semantic, structural and hierarchical model. In: Proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis, vol. 6669, pp. 17–24 (2011)Google Scholar
  16. 16.
    de las Heras, L.P., Valveny, E., Sánchez, G.: Combining structural and statistical strategies for unsupervised wall detection in floor plans. In: Proceedings of the 10th IAPR International Workshop on Graphics Recognition, pp. 123–128 (2013)Google Scholar
  17. 17.
    Elkan, C.: Using the triangle inequality to accelerate k-means. In: Proceedings of the 20th International Conference on Machine Learning, pp. 147–153 (2003)Google Scholar
  18. 18.
    Escalera, S., Fornes, A., Pujol, O., Escudero, A., Radeva, P.: Circular blurred shape model for symbol spotting in documents. In: Proceedings of the 26th IEEE International Conference on Image Processing, pp. 2005–2008 (2009)Google Scholar
  19. 19.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88, 303–338 (2010)CrossRefGoogle Scholar
  20. 20.
    Hori, O., Tanigawa, S.: Raster-to-vector conversion by line fitting based on contours and skeletons. In: Proceedings of the Second International Conference on Document Analysis and Recognition, pp. 353–358 (1993)Google Scholar
  21. 21.
    Jiang, X., Bunke, H.: An optimal algorithm for extracting the regions of a plane graph. Pattern Recogn. Lett. 14(7), 553–558 (1993)CrossRefMATHGoogle Scholar
  22. 22.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)Google Scholar
  23. 23.
    Lladós, J., López-Krahe, J., Martí, E.: A system to understand hand-drawn floor plans using subgraph isomorphism and hough transform. Mach. Vis. Appl. 10, 150–158 (1997)CrossRefGoogle Scholar
  24. 24.
    Lladós, J., Sánchez, G., Martí, E.: A string based method to recognize symbols and structural textures in architectural plans. In: Graphics Recognition Algorithms and Systems, Lecture Notes in Computer Science, vol. 1389, pp. 91–103. Springer, Berlin (1998)Google Scholar
  25. 25.
    Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  26. 26.
    Lu, T., Yang, H., Yang, R., Cai, S.: Automatic analysis and integration of architectural drawings. Int. J. Doc. Anal. Recogn. 9, 31–47 (2007)CrossRefGoogle Scholar
  27. 27.
    Macé, S., Locteau, H., Valveny, E., Tabbone, S.: A system to detect rooms in architectural floor plan images. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, DAS ’10, pp. 167–174 (2010)Google Scholar
  28. 28.
    Or, S.H., Wong, K.H., Yu, Y.K., Chang, M.M.Y.: Highly automatic approach to architectural floorplan image understanding & model generation. iN: Proceedings of the Vision, Modeling, and Visualization, pp. 25–32 (2005)Google Scholar
  29. 29.
    Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar
  30. 30.
    Phillips, I., Chhabra, A.: Empirical performance evaluation of graphics recognition systems. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 849–870 (1999) Google Scholar
  31. 31.
    Rendek, J., Masini, G., Dosch, P., Tombre, K.: The search for genericity in graphics recognition applications: design issues of the qgar software system. In: Document Analysis Systems VI. Lecture Notes in Computer Science, vol. 3163, pp. 366–377 (2004)Google Scholar
  32. 32.
    Ryall, K., Shieber, S., Marks, J., Mazer, M.: Semi-automatic delineation of regions in floor plans. In: Proceedings of the Third International Conference on Document Analysis and Recognition, pp. 964–983 (1995)Google Scholar
  33. 33.
    Santosh, K., Lamiroy, B., Wendling, L.: Integrating vocabulary clustering with spatial relations for symbol recognition. Int. J. Doc. Anal. Recogn. (IJDAR), 1–18 (2013)Google Scholar
  34. 34.
    Tombre, K., Tabbone, S., Pélissier, L., Lamiroy, B., Dosch, P.: Text/graphics separation revisited. In: Document Analysis Systems V, Lecture Notes in Computer Science, pp. 615–620 (2002)Google Scholar
  35. 35.
    Weber, M., Liwicki, M., Dengel, A.: a.Scatch—a sketch-based retrieval for architectural floor plans. In: Proceedings of the 12th International Conference on Frontiers of Handwriting Recognition, pp. 289–294 (2010)Google Scholar
  36. 36.
    Wessel, R., Blümel, I., Klein, R.: The room connectivity graph: shape retrieval in the architectural domain. In: Proceedings of the 16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (2008)Google Scholar
  37. 37.
    Zhi, G., Lo, S., Fang, Z.: A graph-based algorithm for extracting units and loops from architectural floor plans for a building evacuation model. Comput. Aided Des. 35(1), 1–14 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Lluís-Pere de las Heras
    • 1
  • Sheraz Ahmed
    • 2
  • Marcus Liwicki
    • 2
  • Ernest Valveny
    • 1
  • Gemma Sánchez
    • 1
  1. 1.Computer Vision CenterBarcelonaSpain
  2. 2.German Research Center for AI (DFKI)KaiserslauternGermany

Personalised recommendations