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Gable Roof Description by Self-Avoiding Polygon

  • Qiongchen Wang
  • Zhiguo Jiang
  • Junli Yang
  • Danpei Zhao
  • Zhenwei Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)

Abstract

In this paper, we present a Self-Avoiding Polygon (SAP) model for describing and detecting complex gable rooftops from nadir-view aerial imagery. We demonstrate that a broad range of gable rooftop shapes can be summarized as self-avoiding polygons, whose vertices correspond to roof corners. The SAP model, defined over the joint space of all possible SAPs and images, combines the shape prior embedded in SAP and a set of appearance features (edge, color and texture) learned from training images. Given an observed image, the posterior probability of the SAP model measures how well each SAP fits the observed data. Our inference algorithm follows the MAP framework, i.e. detecting the best gable roof is equivalent to finding the optimal self-avoiding polygon on the image plain. Even though the entire state space of all SAPs is enormous, we find that by using A * search, commonly our algorithm can find the optimal solution in polynominal time. Experiments on a set of challenging image shows promising performance.

Keywords

Gable Roof Self-Avoiding Polygon (SAP) 

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References

  1. 1.
    Kim, Z., Nevatia, R.: Automatic description of complex buildings from multiple images. Computer Vision and Image Understanding 96(1), 60–95 (2004)CrossRefGoogle Scholar
  2. 2.
    Fischer, A., Kolbe, T.H., Lang, F., Cremers, A.B., Forstner, W., Pluemer, L., Steinhage, V.: Extracting buildings from aerial images using hierarchical aggregation in 2D and 3D. Computer Vision and Image Understanding 72(2), 185–203 (1998)CrossRefGoogle Scholar
  3. 3.
    Bignone, F., Henricsson, O., Fua, P., Stricker, M.: Automatic extraction of generic house roofs from high resolution aerial imagery. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 83–96. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  4. 4.
    Scholze, S., Moons, T., Gool, L.J.V.: A probabilistic approach to roof patch extraction and reconstruction. In: Automatic Extraction of Man-Made Objects from Aerial and Space Image, III, pp. 195–204 (2001)Google Scholar
  5. 5.
    Bousquet-Melou, M.: Convex polyominoes and heaps of segments. Journal of Physics A: Mathematical and General 25, 1925–1934 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Dollar, P., Tu, Z.W., Belongie, S.J.: Supervised learning of edges and object boundaries. In: CVPR (2006)Google Scholar
  7. 7.
    Wu, Y.N., Si, Z.Z., Fleming, C., Zhu, S.C.: Deformable template as active basis. In: ICCV, pp. 1–8 (2007)Google Scholar
  8. 8.
    Mayer, H.: Automatic object extraction from aerial imagery: A survey focusing on buildings. Computer Vision and Image Understanding 74(2), 138–149 (1999)CrossRefGoogle Scholar
  9. 9.
    Chellappa, R., Davis, L.S., Lin, C.L., Moore, T., Rodriguez, C., Rosenfeld, A., Zhang, X., Zheng, Q.: Site model supported monitoring of aerial images. In: CVPR, pp. 694–699 (1994)Google Scholar
  10. 10.
    Lin, C., Nevatia, R.: Building detection and description from a single intensity image. Computer Vision and Image Understanding 72(2), 101–121 (1998)CrossRefGoogle Scholar
  11. 11.
    Katartzis, A., Sahli, H.: A stochastic framework for the identification of building rooftops using a single remote sensing image. IEEE Trans. Geoscience and Remote Sensing 46(1), 259–271 (2008)CrossRefGoogle Scholar
  12. 12.
    Russel, S., Norvig, P.: Artificial Intelligence: a Modern Approach. Prentice-Hall, Englewood Cliffs (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qiongchen Wang
    • 1
  • Zhiguo Jiang
    • 1
  • Junli Yang
    • 1
  • Danpei Zhao
    • 1
  • Zhenwei Shi
    • 1
  1. 1.Image CenterBeihang UniversityBeijingChina

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