Skip to main content
SpringerLink
Log in

Building Detection and 3D Reconstruction from Two-View of Monocular Camera

  • Conference paper
Computational Collective Intelligence. Technologies and Applications (ICCCI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6922))

Included in the following conference series:

  • 1660 Accesses

Abstract

This paper proposes a method for building detection and 3D reconstruction from two-view by using monocular system. According to this method, building faces are detected by using color, straight line, edge and vanishing point. In the next step, invariant features are extracted and matching to find fundamental matrix. Three-dimension reconstruction of building is implemented based on camera matrixes which are computed from fundamental matrix and camera calibration parameters (essential matrix). The true dimension of building will be obtained if assume the baseline of monocular system is known. The simulation results will demonstrate the effectiveness of this method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Longuet-Higgins, H.C.: A computer algorithm for reconstructing a scene from two projections. Nature 293, 133–135 (1981)

    Article  Google Scholar 

  2. Hartley, R.I.: Estimation of relative camera positions for uncalibrated cameras. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588. Springer, Heidelberg (1992)

    Google Scholar 

  3. Hartley, R.I.: Euclidean reconstruction from uncalibrated views. In: Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision, pp. 237–256. Springer, London (1994)

    Google Scholar 

  4. Heyden, A., Astrom, K.: Euclidean reconstruction from constant intrinsic parameters. In: ICPR 1996: Proceedings of the 1996 International Conference on Pattern Recognition (ICPR 1996), vol. I, p. 339. IEEE Computer Society, Washington, DC, USA (1996)

    Google Scholar 

  5. Bougnoux, S.: From projective to euclidean space under any practical situation, a criticism of self-calibration. In: ICCV 1998: Proceedings of the Sixth International Conference on Computer Vision, p. 790. IEEE Computer Society, Washington, DC, USA (1998)

    Google Scholar 

  6. Sturm, P.: A case against kruppa’s equations for camera self-calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1199–1204 (2000)

    Article  Google Scholar 

  7. Ma, Y., Koeka, J., Sastry, S.: Optimization criteria and geometric algorithms for motion and structure estimation. Int. J. Comput. Vision 44(3), 219–249 (2001)

    Article  MATH  Google Scholar 

  8. Kanatani, K., Nakatsuji, A., Sugaya, Y.: Stabilizing the focal length computation for 3-D reconstruction from two uncalibrated views. Int. J. Comput. Vision 66(2), 109–122 (2006)

    Article  Google Scholar 

  9. Caprile, B., Torre, V.: Using vanishing points for camera calibration. International Journal of Computer Vision 4, 127–140 (1990)

    Article  Google Scholar 

  10. Triggs, B.: Autocalibration from planar scenes. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, p. 89. Springer, Heidelberg (1998)

    Google Scholar 

  11. Cipolla, R., Drummond, T., Robertson, D.: Calibration from vanishing points in image of architectural scenes. In: The 10th British Machine Vision Conference (1999)

    Google Scholar 

  12. Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)

    Article  Google Scholar 

  13. Svedberg, D., Carlsson, S.: Calibration, pose and novel views from single images of constrained scenes. Pattern Recogn. Lett. 21(13-14), 1125–1133 (2000)

    Article  MATH  Google Scholar 

  14. Kǒsecká, J., Zhang, W.: Video compass. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 476–490. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  15. Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vision 8(2), 123–151 (1992)

    Article  Google Scholar 

  16. Luong, Q.-T., Faugeras, O.D.: The fundamental matrix: theory, algorithms, and stability analysis. Int. J. Comput. Vision 17(1), 43–75 (1996)

    Article  Google Scholar 

  17. Zeller, C., Faugeras, O.D.: Camera self-calibration from video sequences: the kruppa equations revisited. Technical Report RR-2793, INRIA, France (1996)

    Google Scholar 

  18. Luong, Q.-T., Faugeras, O.D.: Self-calibration of a moving camera from point correspondences and fundamental matrices. Int. J. Comput. Vision 22(3), 261–289 (1997)

    Article  Google Scholar 

  19. Ma, Y., Vidal, R., Kǒsecká, J., Sastry, S.: Kruppa equation revisited: Its renormalization and degeneracy. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 561–577. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  20. Hartley, R.I., Silpa-Anan: Reconstruction from two views using approximate calibration. In: Proceedings 5th Asian Conf. Computer Vision, Melbourne, Australia, vol. 1, pp. 338–343 (2002)

    Google Scholar 

  21. Pollefeys, M., Koch, R., Gool, L.V.: Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. Int. J. Comput. Vision 32(1), 7–25 (1999)

    Article  Google Scholar 

  22. Sturm, P.: On focal length calibration from two views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, vol. II, pp. 145–150. IEEE Computer Society Press, Los Alamitos (2001)

    Google Scholar 

  23. Sturm, P., Cheng, Z., Chao, P.C., Neow Poo, A.: Focal length calibration from two views: method and analysis of singular cases. Computer Vision and Image Understanding 99(1), 58–95 (2005)

    Article  Google Scholar 

  24. Trinh, H.H., Kim, D.N., Jo, K.H.: Supervised Training Database for Building Recognition by Using Cross Ratio Invariance and SVD-based Method. International Journal of Applied Intelligence 32(2), 216–230 (2010)

    Article  Google Scholar 

  25. Lowe, D.: Object recognition from local scale-invariant features. In: Proc. of the International Conference on Computer Vision, pp. 1150–1157 (1999)

    Google Scholar 

  26. Lowe, D.: Distinctive Image Features from Scale-Invariant Interest Points. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  27. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004) ISBN: 0521540518

    Book  MATH  Google Scholar 

  28. Trinh, H.H., Jo, K.H.: Image-based Structural Analysis of Building Using Line Segments and Their Geometrical Vanishing Points. In: Proceeding of SICE-ICASE (2006)

    Google Scholar 

  29. Trinh, H.H., Kim, D.N., Jo, K.H.: Facet-based Multiple Building Analysis for Robot Intelligence. Journal of Applied Mathematics and Computation (AMC) 205(2), 537–549 (2008)

    Article  MATH  Google Scholar 

  30. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM 395, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  31. Harris, C., Stephens, M.: A combined corner and edge detector, in Proceedings of the 4th Alvey Vision Conference, Manchester, UK, pp. 147–151 (1998)

    Google Scholar 

  32. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  33. Juan, L., Gwun, O.: A Comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing 3(5) (2010)

    Google Scholar 

  34. Jean-Yves, Bouguet: Camera Calibration Toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc/index.html

Download references

Author information

Authors and Affiliations

  1. Graduated School of Electrical Engineering, University of Ulsan, Ulsan, Korea

    My-Ha Le & Kang-Hyun Jo

Authors
  1. My-Ha Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kang-Hyun Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Morska 81-87, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz

  2. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. University of Information Technology Vietnam, Km 20, Xa lo Ha Noi, Linh Trung, Thu Duc, 848, HCM City, Vietnam

    Kiem Hoang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Le, MH., Jo, KH. (2011). Building Detection and 3D Reconstruction from Two-View of Monocular Camera. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23935-9_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23934-2

  • Online ISBN: 978-3-642-23935-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation