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A Parametric Algorithm for Skyline Extraction

  • Mehdi AyadiEmail author
  • Loreta Suta
  • Mihaela Scuturici
  • Serge Miguet
  • Chokri Ben Amar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

Abstract

This paper is dedicated to the problem of automatic skyline extraction in digital images. The study is motivated by the needs, expressed by urbanists, to describe in terms of geometrical features, the global shape created by man-made buildings in urban areas. Skyline extraction has been widely studied for navigation of Unmanned Aerial Vehicles (drones) or for geolocalization, both in natural and urban contexts. In most of these studies, the skyline is defined by the limit between sky and ground objects, and can thus be resumed to the sky segmentation problem in images. In our context, we need a more generic definition of skyline, which makes its extraction more complex and even variable. The skyline can be extracted for different depths, depending on the interest of the user (far horizon, intermediate buildings, near constructions, ...), and thus requires a human interaction. The main steps of our method are as follows: we use a Canny filter to extract edges and allow the user to interact with filter’s parameters. With a high sensitivity, all the edges will be detected, whereas with lower values, only most contrasted contours will be kept by the filter. From the obtained edge map, an upper envelope is extracted, which is a disconnected approximation of the skyline. A graph is then constructed and a shortest path algorithm is used to link discontinuities. Our approach has been tested on several public domain urban and natural databases, and have proven to give better results that previously published methods.

Keywords

Augmented Reality Short Path Algorithm Ground Truth Image Mobile Augmented Reality Image Segmentation Problem 
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.

Notes

Acknowledgments

This work was part of the “ANR-12-VBDU-0008 - Skyline” project, funded by the “Agence Nationale de la Recherche (ANR)” and the Labex (Laboratoire d’Excellence) “Intelligence des mondes Urbains (IMU)”.

References

  1. 1.
    Johns, D., Dudek, G.: Urban position estimation from one dimensional visual cues. In: 3rd Canadian Conference on Computer and Robot Vision (CRV 2006), pp. 22–22. IEEE (2006)Google Scholar
  2. 2.
    Yusoff, N.A.H., Noor, A.M., Ghazali, R.: City skyline conservation: sustaining the premier image of Kuala Lumpur. Procedia Environ. Sci. 20, 583–592 (2014). Elsevier B.VCrossRefGoogle Scholar
  3. 3.
    Fang, M., Chiu, M.-Y., Liang, C.-C., Singh, A.: Skyline for video-based virtual rail for vehicle navigation. In: Proceedings of the Intelligent Vehicles 1993 Symposium, pp. 207–212. IEEE (1993)Google Scholar
  4. 4.
    Byung-Ju, K., Jong-Jin, S., Hwa-Jin, N., Jin-Soo, K.: Skyline extraction using a multistage edge filtering. Int. J. Electr. Comput. Energ. Electron. Commun. Eng. 5, 10–14 (2011)Google Scholar
  5. 5.
    Jiebo, L., Etz, S.P.: A physical model-based approach to detecting sky in photographic images. IEEE Trans. Image Process. 11, 201–212 (2002)CrossRefGoogle Scholar
  6. 6.
    Saurer, O., Baatz, G., Köser, K., Ladický, U., Pollefeys, M.: Image based geo-localization in the Alps. Int. J. Comput. Vis. 116, 213–225 (2016)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Baatz, G., Saurer, O., Koser, K., Pollefeys, M.: Large scale visual geo-localization of images in mountainous terrain. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7573, pp. 517–530. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Lie, W., Lin, T.C.-I., Lin, T., Hung, K.-S.: A robust dynamic programming algorithm to extract skyline in images for navigation. Pattern Recogn. Lett. 26, 221–230 (2005)CrossRefGoogle Scholar
  9. 9.
    Yang, S.W., Kim, I.C., Kim, J.S.: Robust skyline extraction algorithm for mountainous images. In: Proceedings of the Second International Conference on Computer Vision Theory and Applications, pp. 253–257. SciTePress - Science and and Technology Publications (2007)Google Scholar
  10. 10.
    Bazin, J.-C., Kweon, I., Demonceaux, C., Vasseur, P.: Dynamic programming and skyline extraction in catadioptric infrared images. In: IEEE International Conference on Robotics and Automation, pp. 409–416. IEEE (2009)Google Scholar
  11. 11.
    Meguro, H.-I., Murata, T., Amano, Y., Hasizume, T., Takiguchi, J.-I.: Development of a positioning technique for an urban area using omnidirectional infrared camera and aerial survey data. Adv. Robot. 22, 731–747 (2008)CrossRefGoogle Scholar
  12. 12.
    Ramalingam, S., Bouaziz, S., Sturm, P., Brand, M.: SKYLINE2GPS: localization in urban canyons using omni-skylines. In International Conference on Intelligent Robots and Systems, pp. 3816–3823. IEEE (2010)Google Scholar
  13. 13.
    Ramalingam, S., Bouaziz, S., Sturm, P., Brand, M.: Geolocalization using skylines from omni-images. In: the 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 23–30. IEEE (2009)Google Scholar
  14. 14.
    Zhu, S., Morin, L., Pressigout, M., Moreau, G., Servieres, M.: Video/GIS registration system based on skyline matching method. In: International Conference on Image Processing, pp. 3632–3636. IEEE (2013)Google Scholar
  15. 15.
    Fukuda, T., Zhang, T., Yabuki, N.: Improvement of registration accuracy of a handheld augmented reality system for urban landscape simulation. Front. Architectural Res. 3, 386–397 (2014)CrossRefGoogle Scholar
  16. 16.
    Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15555-0_26 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mehdi Ayadi
    • 1
    • 2
    Email author
  • Loreta Suta
    • 1
  • Mihaela Scuturici
    • 1
  • Serge Miguet
    • 1
  • Chokri Ben Amar
    • 2
  1. 1.University of Lyon, CNRS University Lyon 2 LIRIS, UMR 5205LyonFrance
  2. 2.REGIM-Lab: REsearch Groups in Intelligent MachinesUniversity of Sfax, ENISSfaxTunisia

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