Skip to main content

SURFLogo - Mobile Tagging with App Icons

  • Conference paper
Mobile Computing, Applications, and Services (MobiCASE 2015)

Abstract

Mobile tagging became more and more popular in commercials, magazines, newspapers, and other applications during the last years. In context of commercials, a bar code containing the advertisers internet address is often used to refer a customer to related online content. Due to their robustness as well as their comparably high fault-tolerance in case of low quality pictures, QR-Code systems are commonly used for that task. Connected to that topic we present a special procedure for mobile tagging, which uses a distinct logo or image in order to refer to certain information instead of a QR-Code. Our procedure was optimized to work with a conventional smartphone – the only prerequisite for usage is the possession of a smartphone capable of capturing and analyzing the different logos with our smartphone application. To match the logos with related information and to determine their uniqueness we introduce a new similarity measure on basis of SURF feature points and a contour comparison.

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

Access this chapter

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 EPUB and 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

References

  1. Al-Khalifa, H.S.: Utilizing QR code and mobile phones for blinds and visually impaired people. In: Miesenberger, K., Klaus, J., Zagler, W.L., Karshmer, A.I. (eds.) ICCHP 2008. LNCS, vol. 5105, pp. 1065–1069. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  2. Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 510–517 (2012)

    Google Scholar 

  3. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Canadi, M., Hpken, W., Fuchs, M.: Application of QR codes in online travel distribution. In: Gretzel, U., Law, R., Fuchs, M. (eds.) Information and Communication Technologies in Tourism 2010, pp. 137–148. Springer, Vienna (2010)

    Chapter  Google Scholar 

  6. Chandrasekhar, V., Chen, D.M., Lin, A., Takacs, G., Tsai, S.S., Cheung, N.-M., Reznik, Y., Grzeszczuk, R., Girod, B.: Comparison of local feature descriptors for mobile visual search. In: 17th IEEE International Conference on Image Processing (ICIP 2010), pp. 3885–3888 (2010)

    Google Scholar 

  7. Darianian, M., Michael, M.: Smart home mobile RFID-based internet-of-things systems and services. In: International Conference on Advanced Computer Theory and Engineering, ICACTE 2008, pp. 116–120, December 2008

    Google Scholar 

  8. Gast, M.S.: Building Applications with iBeacon: Proximity and Location Services with Bluetooth Low Energy. O’Reilly Media, Sebastopol (2014)

    Google Scholar 

  9. Goosen, C.A.: Design and implementation of a bluetooth 4.0 le infrastructure for mobile devices, June 2014

    Google Scholar 

  10. Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)

    Article  MATH  Google Scholar 

  11. Hegen, M.: Mobile Tagging: Potenziale für das Mobile Business. Diplom.de (2010)

    Google Scholar 

  12. Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  13. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV 2011), pp. 2548–2555. IEEE (2011)

    Google Scholar 

  14. Madlmayr, G., Scharinger, J.: Neue dimension von mobilen tourismusanwendungen durch near field communication-technologie. In: Egger, R., Jooss, M. (eds.) mTourism, pp. 75–88. Gabler (2010)

    Google Scholar 

  15. Microsoft: Mircosoft Tag - Creating Custom Tags (2011). http://tag.microsoft.com/what-is-tag/custom-tags.aspx. Accessed 16 July 2015

  16. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proceedings of Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 525–531. IEEE (2001)

    Google Scholar 

  17. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  18. Miksik, O., Mikolajczyk, K.: Evaluation of local detectors and descriptors for fast feature matching. In: 21st International Conference on Pattern Recognition (ICPR 2012), pp. 2681–2684 (2012)

    Google Scholar 

  19. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision (ICCV 2011), pp. 2564–2571 (2011)

    Google Scholar 

  20. Schaffalitzky, F., Zisserman, A.: Automated scene matching in movies. In: Lew, M., Sebe, N., Eakins, J.P. (eds.) CIVR 2002. LNCS, vol. 2383, pp. 186–197. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  21. Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proceedings of Ninth IEEE International Conference on Computer Vision, pp. 1470–1477 (2003)

    Google Scholar 

  22. Tan, G.W.-H., Ooi, K.-B., Chong, S.-C., Hew, T.-S.: NFC mobile credit card: the next frontier of mobile payment? Telematics Inform. 31(2), 292–307 (2014)

    Article  Google Scholar 

  23. Turcot, P., Lowe, D.G.: Better matching with fewer features: the selection of useful features in large database recognition problems. In: IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops 2009), pp. 2109–2116 (2009)

    Google Scholar 

  24. Walsh, A.: Blurring the boundaries between our physical and electronic libraries. Electron. Libr. 29(4), 429–437 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chadly Marouane .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Marouane, C., Ebert, A. (2015). SURFLogo - Mobile Tagging with App Icons. In: Sigg, S., Nurmi, P., Salim, F. (eds) Mobile Computing, Applications, and Services. MobiCASE 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 162. Springer, Cham. https://doi.org/10.1007/978-3-319-29003-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-29003-4_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29002-7

  • Online ISBN: 978-3-319-29003-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics