Image-Based Delineation of Built Heritage Masonry for Automatic Classification

  • Noelia Oses
  • Fadi Dornaika
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7950)

Abstract

The development of new built heritage assessment protocols that objectivise and standardise the protection process has made it possible to start the work on the algorithms necessary to implement a built heritage analysis and classification ICT tool. The built heritage that will be assessed using these protocols consists of stone masonry constructions. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. Many of the features analysed can be characterised geometrically and are often related to the arrangement of the construction blocks. This paper presents the ground work carried out to make this tool possible: the semi-automatic delineation of the masonry. The validity of this delineation will be shown using the classification results for the analysis of one of the elements assessed in the protocol for masonry bridges.

Keywords

semi-automatic masonry delineation image processing classification built heritage analysis Hough Transform 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kersten, T.P., Lindstaedt, M.: Image-based low-cost systems for automatic 3D recording and modelling of archaeological finds and objects. In: Ioannides, M., Fritsch, D., Leissner, J., Davies, R., Remondino, F., Caffo, R. (eds.) EuroMed 2012. LNCS, vol. 7616, pp. 1–10. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Moussa, W., Abdel-Wahab, M., Fritsch, D.: Automatic fusion of digital images and laser scanner data for heritage preservation. In: Ioannides, M., Fritsch, D., Leissner, J., Davies, R., Remondino, F., Caffo, R. (eds.) EuroMed 2012. LNCS, vol. 7616, pp. 76–85. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Stanco, F.: Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration, and Reconstruction of Ancient Artworks. CRC Press (2011)Google Scholar
  4. 4.
    Godin, G., Beraldin, J.A., Taylor, J., Cournoyer, L., Rioux, M., El-Hackim, S., Baribeau, R., Blais, F., Boulanger, P., Domey, J., Picard, M.: Active optical 3-D imaging for heritage applications. IEEE Computer Graphics and Applications (2002)Google Scholar
  5. 5.
    Muller, P., Wonka, P., Haegler, S., Ulmer, A., Gool, L.V.: Procedural modeling of buildings. ACM Transactions on Graphics 25(3), 614–623 (2006)CrossRefGoogle Scholar
  6. 6.
    Pasko, G., Pasko, A., Vilbrandt, T., Filho, A., da Silva, J.: Digital interpretation of cultural heritage: 3D modeling and materialization of 2D artworks for future museums. International Journal of the Inclusive Museum 3(1), 63–80Google Scholar
  7. 7.
    Remondino, F.: Heritage recording and 3D modeling with photogrammetry and 3D scanning. Remote Sensing 3, 1104–1138 (2011)CrossRefGoogle Scholar
  8. 8.
    Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., Shade, J., Fulk, D.: The digital michelangelo project: 3d scanning of large statues. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2000, pp. 131–144 (2000)Google Scholar
  9. 9.
    Ikeuchi, K., Oishi, T., Takamatsu, J., Sagawa, R., Nakazawa, A., Kurazume, R., Nishino, K., Kamakura, M., Okamoto, Y.: The great buddha project: Digitally archiving, restoring, and analyzing cultural heritage objects. International Journal of Computer Vision 75(1), 189–208 (2007)CrossRefGoogle Scholar
  10. 10.
    Rodríguez, A., Valle, J., Martínez, J.: 3d line drawing from point clouds using chromatic stereo and shading. In: Proceedings of the 14th International Conference on Virtual Systems and Multimedia, pp. 77–84 (2008)Google Scholar
  11. 11.
    Wang, W.: Rock Particle Image Segmentation and Systems. In: Pattern Recognition Techniques, Technology and Applications. I-Tech, Vienna, Austria, pp. 197–226 (2008)Google Scholar
  12. 12.
    Telea, A.: An image inpainting technique based on the fast marching method. Journal of Graphics Tools 9, 25–36 (2004)CrossRefGoogle Scholar
  13. 13.
    Kiryati, N., Eldar, Y., Bruckstein, A.: A probabilistic hough transform. Pattern Recognition 24(4), 303–316 (1991)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)Google Scholar
  15. 15.
    Zhang, W., Qin, Z., Wan, T.: Image scene categorization using multi-bag-of-features. In: International Conference on Machine Learning and Cybernetics (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Noelia Oses
    • 1
  • Fadi Dornaika
    • 2
    • 3
  1. 1.Fundación Zain FundazioaVitoria-GasteizSpain
  2. 2.University of the Basque Country UPV/EHUSan SebastianSpain
  3. 3.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain

Personalised recommendations