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Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery

  • Patrick Denis
  • James H. Elder
  • Francisco J. Estrada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)

Abstract

We address the problem of efficiently estimating the rotation of a camera relative to the canonical 3D Cartesian frame of an urban scene, under the so-called “Manhattan World” assumption [1,2]. While the problem has received considerable attention in recent years, it is unclear how current methods stack up in terms of accuracy and efficiency, and how they might best be improved. It is often argued that it is best to base estimation on all pixels in the image [2]. However, in this paper, we argue that in a sense, less can be more: that basing estimation on sparse, accurately localized edges, rather than dense gradient maps, permits the derivation of more accurate statistical models and leads to more efficient estimation. We also introduce and compare several different search techniques that have advantages over prior approaches. A cornerstone of the paper is the establishment of a new public groundtruth database which we use to derive required statistics and to evaluate and compare algorithms.

Keywords

Ground Truth Camera Parameter Urban Scene Vanishing Point Gauss Sphere 
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.

References

  1. 1.
    Coughlan, J.M., Yuille, A.L.: Manhattan world: Compass direction from a single image by bayesian inference. In: Seventh International Conference on Computer Vision, vol. 2, pp. 941–947. IEEE, Los Alamitos (1999)CrossRefGoogle Scholar
  2. 2.
    Coughlan, J.M., Yuille, A.L.: Manhattan world: Orientation and outlier detection by bayesian inference. Neural Computation 15(5), 1063–1088 (2003)CrossRefGoogle Scholar
  3. 3.
    Deutscher, J., Isard, M., MacCormick, J.: Automatic camera calibration from a single manhattan image. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 175–188. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Schindler, G., Dellaert, F.: Atlanta world: An expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I–203 – I–209. IEEE, Los Alamitos (2004) Google Scholar
  5. 5.
    Kos̆ecká, J., Zhang, W.: Video compass. In: Seventh European Conference on Computer Vision, pp. 476–490 (2002)Google Scholar
  6. 6.
    Wildenauer, H., Vincze, M.: Vanishing point detection in complex man-made worlds. In: 14th IEEE International Conference on Image Analysis and Processing, pp. 615–622. IEEE, Los Alamitos (2007)Google Scholar
  7. 7.
    Collins, R., Weiss, R.: Vanishing point calculation as a statistical inference on the unit sphere. In: Third International Conference on Computer Vision, pp. 400–403. IEEE, Los Alamitos (1990)Google Scholar
  8. 8.
    Kanatani, K.: Geometric Computation for Machine Vision. Oxford University Press, Inc., New York (1993)zbMATHGoogle Scholar
  9. 9.
    Elder, J.H., Zucker, S.W.: Local scale control for edge detection and blur estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(7), 699–716 (1998)CrossRefGoogle Scholar
  10. 10.
    Avriel, M.: Nonlinear Programming: Analysis and Methods. Prentice-Hall Inc., Englewood Cliffs (1976)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Patrick Denis
    • 1
  • James H. Elder
    • 1
  • Francisco J. Estrada
    • 2
  1. 1.York UniversityCanada
  2. 2.University of TorontoCanada

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