Impact of the Textbooks’ Graphic Design on the Augmented Reality Applications Tracking Ability

  • N. Kulishova
  • N. Suchkova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


Augmented reality (AR) is very effective in school education. Thus, a number of these applications are growing permanently. In most cases, these applications use school textbooks as target images for the AR technology. In developing a textbook design accompanied by the AR application, it is important to use such elements will ensure a stable tracking property when a gadget is held by a kid. There are also various graphic design elements as addition to texts and illustrations in modern textbooks. It is necessary to study how these elements ensure the tracking stability when conditions of textbook viewing are changing. The use of corner detectors to assess the tracking ability of different graphic elements in a textbook is considered. A comparative analysis of the tracking stability for textbook pages is carried out by means of the Harris-Stephens method, BRISK, FAST, Shi & Tomasi methods (also known as the minimum eigenvalue algorithm) which detect features and form their descriptors for the image. Results for these methods are collated with the tracking ability of targets used for a rating estimation on the basis of the Augmented Reality Platform Qualcomm Vuforia.


Augmented reality Features Tracking Corner detectors  Textbook School education 


  1. 1.
    Azuma, R.T.: A survey of augmented reality. Presence Teleoperators Virtual Env. 6(4), 355–385 (1997). The MIT Press, Cambridge, MACrossRefGoogle Scholar
  2. 2.
    Hu, Z., Bodyanskiy, Y.V., Kulishova, N.Y., Tyshchenko, O.K.: A multidimensional extended neo-fuzzy neuron for facial expression recognition. Int. J. Intell. Syst. Appl. (IJISA) 9(9), 29–36 (2017). Scholar
  3. 3.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Fuzzy clustering data given in the ordinal scale. Int. J. Intell. Syst. Appl. (IJISA) 9(1), 67–74 (2017). Scholar
  4. 4.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Fuzzy clustering data given on the ordinal scale based on membership and likelihood functions sharing. Int. J. Intell. Syst. Appl. (IJISA) 9(2), 1–9 (2017). Scholar
  5. 5.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Possibilistic fuzzy clustering for categorical data arrays based on frequency prototypes and dissimilarity measures. Int. J. Intell. Syst. Appl. (IJISA) 9(5), 55–61 (2017). Scholar
  6. 6.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Tkachov, V.M.: Fuzzy clustering data arrays with omitted observations. Int. J. Intell. Syst. Appl. (IJISA) 9(6), 24–32 (2017). Scholar
  7. 7.
    Bodyanskiy, Y.V., Tyshchenko, O.K., Kopaliani, D.S.: An evolving connectionist system for data stream fuzzy clustering and its online learning. Neurocomputing 262, 41–56 (2017)CrossRefGoogle Scholar
  8. 8.
    Hu, Z., Mashtalir, S.V., Tyshchenko, O.K., Stolbovyi, M.I.: Video shots’ matching via various length of multidimensional time sequences. Int. J. Intell. Syst. Appl. (IJISA) 9(11), 10–16 (2017). Scholar
  9. 9.
    Rodehorst, V., Koschan, A.: Comparison and evaluation of feature point detectors. In: Proceedings of the 5th International Symposium Turkish-German Joint Geodetic Days TGJGD 2006, Berlin (2006)Google Scholar
  10. 10.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  11. 11.
    Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 1508–1511 (2005)Google Scholar
  12. 12.
    Leutenegger, S., Chli, M., Siegwart, R: BRISK: Binary Robust Invariant Scalable Keypoints. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2011)Google Scholar
  13. 13.
    Shi, J., Tomasi, C.: Good features to track. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar
  14. 14.
    Amin, D., Govilkar, S.: Comparative study of augmented reality SDK’s. Int. J. Comput. Sci. Appl. (IJCSA) 5(1), 11–26 (2015)Google Scholar
  15. 15.
    Qualcomm Vuforia Core Samples. 2 Feb 2015

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Kharkov National University of RadioelectronicsKharkivUkraine

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