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A Spot Navigation System for the Visually Impaired by Use of SIFT-Based Image Matching

  • Hotaka TakizawaEmail author
  • Kazunori Orita
  • Mayumi Aoyagi
  • Nobuo Ezaki
  • Shinji Mizuno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9178)

Abstract

In this report, we propose a spot navigation system to assist visually impaired individuals in recalling memories related to spots that they often visit. This system registers scene images and voice memos that are recorded in advance by a visually impaired individual or his/her sighted supporter at various spots. When the individual visits one of the spots, the system determines the current spot from the results of image matching between the registered images and a query image taken by the individual at the spot, then plays a voice memo which corresponds to the spot. The system is applied to actual indoor and outdoor scenes, and experimental results are shown.

Keywords

Query Image Scale Invariant Feature Transform Mobile System Image Match Matching Result 
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 supported in part by the JSPS KAKENHI Grant Number 25560278.

References

  1. 1.
    World Health Organization, Media Centre, Visual impairment and blindness, Fact Sheet No 282. http://www.who.int/mediacentre/factsheets/fs282/en/
  2. 2.
    Gaude, M., Candolkar, V.: GPS Navigator for Visually Impaired. Int. J. Electron. Signals Syst. (IJESS), vol-2 (2012), ISSN: 2231–5969, ISS-2,3,4Google Scholar
  3. 3.
    Legge, G.E., Beckmann, P.J., Tjan, B.S., Havey, G., Kramer, K., Rolkosky, D., Gage, R., Chen, M., Puchakayala, S., Rangarajan, A.: Indoor navigation by people with visual impairment using a digital sign system. PLoS One 8(10), e76783 (2013)CrossRefGoogle Scholar
  4. 4.
    Zöllner, M., Huber, S., Jetter, H.-C., Reiterer, H.: NAVI – a proof-of-concept of a mobile navigational aid for visually impaired based on the microsoft kinect. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part IV. LNCS, vol. 6949, pp. 584–587. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  5. 5.
    Sekai Camera Support Center BEYOND REALITY. http://support.sekaicamera.com/ja/service
  6. 6.
    e.Typist Mobile. MEDIA DRIVE CORPORATION. http://mediadrive.jp/products/etmi
  7. 7.
    TapTapSee - Blind and Visually Impaired Camera. TapTapSee. http://www.taptapseeapp.com
  8. 8.
    LookTel Recognizer Documentation. LookTel. http://www.looktel.com/recognizer-documentation
  9. 9.
  10. 10.
    Takizawa, H., Yamaguchi, S., Aoyagi, M., Ezaki, N., Mizuno, S.: Kinect cane : an assistive system for the visually impaired based on three-dimensional object recognition. In: The Proceedings of the 2012 IEEE/SICE International Symposium on System Integration, vol. 1, No. 1, pp. 740–745 (2012)Google Scholar
  11. 11.
    Takizawa, H., Yamaguchi, S., Aoyagi, M., Ezaki, N., Mizuno, S.: Kinect cane : object recognition aids for the visually impaired. In: The proceedings of the 6th IEEE International Conference on Human System Interaction (HSI 2013), 6 p. (CDROM proceedings) (2013)Google Scholar
  12. 12.
    Orita, K., Takizawa, H., Aoyagi, M., Ezaki, N., Mizuno, S.: Obstacle detection by the kinect cane system for the visually impaired. In: 2013 IEEE/SICE International Symposium on System Integration (SII 2013), pp. 115–118 (CDROM proceedings) (2013)Google Scholar
  13. 13.
    Hironobu, F.: Gradient-based feature extraction : SIFT and HOG. In: PRMU, CVIM 160, pp. 211–224 (2007)Google Scholar
  14. 14.
    Kameda, Y., ohta, Y.: Image retrieval of first-person vision for pedestrian navigation in urban area. In: ICPR, pp. 364–367 (2010)Google Scholar
  15. 15.
    Kurata, T., Kourogi, M., Ishikawa, T., Kameda, Y., Aoki, K., Ishikawa, J.: Indoor-outdoor navigation system for visually-impaired pedestrians: preliminary evaluation of position measurement and obstacle display. In: Proceedings of ISWC 2011, pp. 123–124 (2011)Google Scholar
  16. 16.
    Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 511–517 (2004)Google Scholar
  17. 17.
    Stein, A., Herbert, M.: Incorporating background invariance into feature-based object recognition. In: Proceedings of IEEE Workshop on Applications of Computer Vision (WACV), pp. 37–44, January 2005Google Scholar
  18. 18.
    Abdel-Hakim, A.E., Farag, A.A.: CSIFT: a SIFT descriptor with color invariant characteristics. In: Proceedings of IEEE Conference on ComputerVision and Pattern Recognition (CVPR), pp. 1978–1983 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hotaka Takizawa
    • 1
    Email author
  • Kazunori Orita
    • 1
  • Mayumi Aoyagi
    • 2
  • Nobuo Ezaki
    • 3
  • Shinji Mizuno
    • 4
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Aichi University of EducationKariyaJapan
  3. 3.Toba National College of Maritime TechnologyTobaJapan
  4. 4.Aichi Institute of TechnologyToyotaJapan

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