Skip to main content
SpringerLink
Log in

Determining the Geographical Location of Image Scenes based on Object Shadow Lengths

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Many studies have addressed various applications of geo-spatial image tagging such as image retrieval, image organisation and browsing. Geo-spatial image tagging can be done manually or automatically with GPS enabled cameras that allow the current position of the photographer to be incorporated into the meta-data of an image. However, current GPS-equipment needs certain time to lock onto navigation satellites and these are therefore not suitable for spontaneous photography. Moreover, GPS units are still costly, energy hungry and not common in most digital cameras on sale. This study explores the potential of, and limitations associated with, extracting geo-spatial information from the image contents. The elevation of the sun is estimated indirectly from the contents of image collections by measuring the relative length of objects and their shadows in image scenes. The observed sun elevation and the creation time of the image is input into a celestial model to estimate the approximate geographical location of the photographer. The strategy is demonstrated on a set of manually measured photographs.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 6
Figure 5
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

Similar content being viewed by others

References

  1. Alvarez, P. (2004). Using Extended File Information (EXIF) file headers in digital evidence analysis, International Journal of Digital Evidence 2(3).

  2. Jang, C.-J., Lee, J.-Y, Lee, J.-W., & Cho, H.-G. (2007). Smart management system for digital photographs using temporal and spatial features with EXIF metadata, presented at 2nd International conference on digital information management, pp. 110-115.

  3. Ahern, S., Naaman, M., Nair, R., & Hui-I Yang, J. (2007). World explorer: visualizing aggregate data from unstructured text in geo-referenced collections, presented at 7th ACM/IEEE-CS joint conference on Digital libraries, pp. 1-10.

  4. D. Carboni, S. Sanna, and P. Zanarini, GeoPix: image retrieval on the geo web, from camera click to mouse click, presented at Proceedings of the 8th conference on Human-computer interaction with mobile devices and services, pp. 169-172, 2006.

  5. GAO, GLOBAL POSITIONING SYSTEM. (2009). Significant Challenges in Sustaining and Upgrading Widely Used Capabilities, United States Government Accountability Office.

  6. Cozman, F., & Krotkov, E. (1995). Robot localization using a computer vision sextant, presented at IEEE International Conference on Robotics and Automation.

  7. Lalonde, J. F., Narasimhan, S. G., & Efros, A. A. (2008). Camera parameters estimation from hand-labelled sun positions in image sequences., Robotics Institute, Carnegie Mellon University. Technical Report CMU-RI-TR-08-32.

  8. Bao, G.-Q., Xiong, S.-S., & Zhou, Z.-Y. (2005). Vision-based horizon extraction for micro air vehicle flight control. IEEE Transactions on Instrumentation and Measurement, 54(3), 1067–1072.

    Article  Google Scholar 

  9. Ettinger, S. M., Nechyba, C., & lfju, P. G. (2002). Towards Flights autonomy: Vision-based horizon detection for micro air vehicles, presented at IEEE International Conference on Robotics and Automation.

  10. Szummer, M., & Picard, R. W. (1998). Indoor-outdoor image classification, presented at IEEE International Workshop on Content-Based Access of Image and Video Database.

  11. Serrano, N., Savakis, A., & Luo, A. (2002). A computationally efficient approach to indoor/outdoor scene classification, presented at 16th International Conference on Pattern Recognition.

  12. Lalonde, J.-F., Narasimhan, S. G., & Efros, A. A. (2008). What does the sky tell us about the camera? Presented at European Conference on Computer Vision.

  13. Michalsky, J. J. (1988). Astronomer’s Almanac algorithm (1950–2050). Solar Energy, 40(3), 227–235.

    Article  Google Scholar 

  14. Horprasert, T., Harwood, D., & Davis, L. S. (1999). A statistical approach for real-time robust background subtraction and shadow detection, presented at IEEE ICCV.

  15. Levine, M. D., & Bhattacharyya, J. (2005). Removing shadows. Pattern Recognition Letters, 26(3), 251–265.

    Article  Google Scholar 

  16. KaewTraKulPong, P., & Bowden, R. (2001). An improved adaptive background mixture model for realtime tracking with shadow detection, presented at 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01.

  17. Lefèvre, S., Mercier, L., Tiberghien, V., & Vincent, N. (2002). Multiresolution color image segmentation applied to background extraction in outdoor images, presented at IS&T European conference on color in graphics, image and vision, 2002.

  18. Li, Y., Sasagawa, T., & Gong, P. (2004). A system of the shadow detection and shadow removal for high resolution city aerial photo, presented at XXth ISPRS Congress.

  19. Wang, J. M., Chung, Y. C., Chang, C. L., & Chen, S. W. (2004). Shadow detection and removal for traffic images, presented at IEEE International Conference on Networking, Sensing and Control.

  20. Sandnes, F. E. (2009). Sorting holiday photos without a GPS: what can we expect from contents-based geo-spatial image tagging? Lecture Notes on Computer Science, 5879, 256–267.

    Article  Google Scholar 

  21. Zheng, Y.-T., Ming, Z., Yang, S., Adam, H., Buddemeier, U., Bissacco, A., et al. (2009). Tour the world: building a web-scale landmark recognition engine, in the proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1085-1092.

  22. Trebi-Ollennu, A., Huntsberger, T., Cheng, Y., & Baumgartner, E. T. (2001). Design and analysis of a sun sensor for planetary rover absolute heading detection. IEEE Transactions on Robotics and Automation, 17(6), 939–947.

    Article  Google Scholar 

  23. Jacobs, N., Satkin, S., Roman, N., Speyer, R., & Pless, R. (2007). Geolocating static cameras, in the proceedings of IEEE 11th International Conference on Computer Vision (ICCV 2007), pp. 1-6.

  24. Jacobs, N., Roman, N., & Pless, R. (2008). Toward fully automatic geo-location and geo-orientation of static outdoor cameras, in the proceedings of IEEE Workshop on Applications of Computer Vision, pp. 1–6, 2008.

  25. Sandnes, F. E. (2010). Where was that photo taken? Deriving geographical information from image collections based on temporal exposure attributes. Multimedia Systems, 16(4–5), 309–318 (This publication is not in print).

    Google Scholar 

  26. Sandnes, F. E. (2010). Unsupervised and fast continent classification of digital image collections using time, in Proceedings of ICSSE 2010, IEEE CS Press, pp. 516–520 (This publication is not in print).

  27. Y-P, Huang, T-W, Chang, Y-R, Chen, & F E, Sandnes. (2008). A back propagation based real-time license plate recognition system. International Journal of Pattern Recognition and Artificial Intelligence, 22(2), 233–251.

    Article  Google Scholar 

  28. Sandnes, F. E. (2010). A simple content-based strategy for estimating the geographical location of a webcam, in Proceedings of PCM2010, Springer Lecture Notes on Computer Science, LNCS, Vol. 6297 (pp. 36-45) (This publication is not in print).

  29. Huang, W., Gao, Y, & Chan, K. L. (2010). A review of region-based image retrieval. Journal of Signal Processing Systems, 59(2), 143–161. doi:10.1007/s11265-008-0294-3 (This publication is not in print).

    Google Scholar 

  30. Heesch, D., & Petrou, M. (2010). Markov random fields with asymmetric interactions for modelling spatial context in structured scene labelling. Journal of Signal Processing Systems, 61(1), 95–103. doi: 10.1007/s11265-009-0349-0 (This publication is not in print).

    Google Scholar 

  31. Jones, L. A., & Condit, H. R. (1941). The brightness scale of exterior scenes and the computation of correct photographic exposure. Journal of the Optical Society of America, 31(11), 651–678.

    Article  Google Scholar 

  32. Ray, S. F. (2000). Camera exposure determination, in the manual of photography: photographic and digital imaging, R. E. Jacobson, S. F. Ray, G. G. Atteridge, & N. R. Axford, (Eds.). Focal Press.

  33. Schlyter, P. (2010). Computing planetary positions - a tutorial with worked examples, Downloaded March 26, 2010 from http://www.stjarnhimlen.se/comp/tutorial.html#5 (This publication is not in print).

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Oslo University College, P.O. Box 4, St. Olavs plass, 0130, Oslo, Norway

    Frode Eika Sandnes

Authors
  1. Frode Eika Sandnes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frode Eika Sandnes.

Additional information

This is a revised and extended version of a paper presented at The Pacific Rim Conference on Multimedia, PCM2009, in Bangkok, December, 2009.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sandnes, F.E. Determining the Geographical Location of Image Scenes based on Object Shadow Lengths. J Sign Process Syst 65, 35–47 (2011). https://doi.org/10.1007/s11265-010-0538-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-010-0538-x

Keywords

Search

Navigation