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Unsupervised Facade Segmentation Using Repetitive Patterns

  • Andreas Wendel
  • Michael Donoser
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)

Abstract

We introduce a novel approach for separating and segmenting individual facades from streetside images. Our algorithm incorporates prior knowledge about arbitrarily shaped repetitive regions which are detected using intensity profile descriptors and a voting–based matcher. In the experiments we compare our approach to extended state–of–the–art matching approaches using more than 600 challenging streetside images, including different building styles and various occlusions. Our algorithm outperforms these approaches and allows to correctly separate 94% of the facades. Pixel–wise comparison to our ground–truth yields a segmentation accuracy of 85%. According to these results our work is an important contribution to fully automatic building reconstruction.

Keywords

Interest Point Panoramic Image Repetitive Pattern Harris Corner Building Facade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Zebedin, L., Klaus, A., Gruber-Geymayer, B., Karner, K.: Towards 3d map generation from digital aerial images. Journal of Photogrammetry and Remote Sensing 60(6), 413–427 (2006)CrossRefGoogle Scholar
  2. 2.
    Mueller, P., Zeng, G., Wonka, P., Gool, L.V.: Image-based procedural modeling of facades. ACM Transactions on Graphics 26(3) (2007)Google Scholar
  3. 3.
    Xiao, J., Fang, T., Zhao, P., Lhuillier, M., Quan, L.: Image-based street-side city modeling. ACM Transactions on Graphics 28(5) (2009)Google Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from Scale-Invariant keypoints. International Journal of Computer Vision (IJCV) 60(2), 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Shechtman, E., Irani, M.: Matching local Self-Similarities across images and videos. In: Proceedings of CVPR (2007)Google Scholar
  6. 6.
    Tell, D., Carlsson, S.: Wide baseline point matching using affine invariants computed from intensity profiles. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 814–828. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Tell, D., Carlsson, S.: Combining appearance and topology for wide baseline matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 68–81. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, vol. 15, p. 50 (1988)Google Scholar
  9. 9.
    Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3(3), 209–226 (1977)zbMATHCrossRefGoogle Scholar
  10. 10.
    Hernandez, J., Marcotegui, B.: Morphological segmentation of building facade images. In: Proceedings of ICIP, p. 4030 (2009)Google Scholar
  11. 11.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based image segmentation. International Journal of Computer Vision (IJCV) 59(2) (2004)Google Scholar
  12. 12.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 22(8), 888–905 (2000)CrossRefGoogle Scholar
  13. 13.
    Korah, T., Rasmussen, C.: Analysis of building textures for reconstructing partially occluded facades. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 359–372. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software 22(4), 469–483 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. International Journal of Computer Vision (IJCV) 74(1), 59–73 (2007)CrossRefGoogle Scholar
  16. 16.
    Rijsbergen, C.J.V.: Information retrieval. Butterworth-Heinemann Newton, MA (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andreas Wendel
    • 1
  • Michael Donoser
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of Technology 

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