Advertisement

Information Technology for Security System Based on Cross Platform Software

  • S. Kondratiuk
  • K. Kruchynin
  • Iu. Krak
  • S. Kruchinin
Conference paper
Part of the NATO Science for Peace and Security Series A: Chemistry and Biology book series (NAPSA)

Abstract

The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units of sign language, animation between states of gesture units with a combination of gestures (words). Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input. With the cross platform means technology achieves the ability to run on multiple platforms without re-implementing for each platform.

Keywords

Cross platform Sing language Dactyl modeling Gesture recognition 

References

  1. 1.
    Mell P, Grance T (2011) The NIST Definition of Cloud Computing (Technical report), National Institute of Standards and Technology: U.S. Department of Commerce.  https://doi.org/10.6028/NIST.SP.800-145. Special publication 800-145
  2. 2.
    The Linux Information Project, Cross-platform DefinitionGoogle Scholar
  3. 3.
    Smith J, Nair R (2005) The architecture of virtual machines. Comput IEEE Comput Soc 38(5):32–38CrossRefGoogle Scholar
  4. 4.
    ASL Sing language dictionary. http://www.signasl.org/sign/model
  5. 5.
    Graschenko LA, Fisun i dr AP (2004) Teoreticheskie i prakticheskie osnovyi cheloveko-kompyuternogo vzaimodeystviya: bazovyie ponyatiya cheloveko-kompyuternyih sistem v informatike i informatsionnoy bezopasnosti: Monografiya, red. A.P. Fisun. OGU, Orel, p 169Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Khan RZ, Ibraheem NA, Meghanathan N et al (eds) (2012) Comparative study of hand gesture recognition system. SIPM, FCST, ITCA, WSE, ACSIT, CS & IT 06:203–213Google Scholar
  9. 9.
    Neff M, Kipp M, Albrecht I, Seidel H-P (2008) Gesture modeling and animation based on a probabilistic re-creation of speaker style. ACM Trans Graph 27(1):24 pages. Article 5. https://doi.org/10.1145/1330511.1330516 CrossRefGoogle Scholar
  10. 10.
    Shapiro A, Chu D, Allen B, Faloutsos P (2005) Dynamic controller toolkit. http://www.arishapiro.com/Sandbox07_DynamicToolkit.pdf
  11. 11.
    Kruvonos IG, Krak YV, Barchukova YV, Trocenko BA (2011) Human hand motion parametrization for dactilemes modeling. J Automat Inform Sci 43(12):1–11Google Scholar
  12. 12.
    Aran O (2006) Sign language tutorial tool. In: eNTERFACE’06, July 17th–August 11th 2006 Dubrovnik, Croatia. Final Project ReportGoogle Scholar
  13. 13.
    Raheja JL (2015) Android based portable hand sign recognition system, Mar 2015.  https://doi.org/10.15579/gcsr.vol3.ch1. Source: arXivGoogle Scholar
  14. 14.
    Stokoe WC (2005) Sign language structure: an outline of the visual communication systems of the American deaf. J Deaf Stud Deaf Educ 10(1):61–67CrossRefGoogle Scholar
  15. 15.
    Unity3D framework. http://www.unity3d.com
  16. 16.
    Tensor flow framework documentation. http://www.tensorflow.org/api/
  17. 17.
    Tubiana R, Thomine J, Mackin E (1996) Examination of the handed wrist, 2nd edn. Martin Dunitz, London. ISBN:1853175447/1-85317-544-7Google Scholar
  18. 18.
    YAML – The Official YAML Web Site. http://www.yaml.org
  19. 19.
    Koller O, Forster J, Ney H (2015) Continuous sign language recognition: towards large vocabulary statistical recognition systems handling multiple signers. Comput Vis Image Underst 141:108–125CrossRefGoogle Scholar
  20. 20.
    Dreuw P, Rybach D, Deselaers T, Zahedi M, Ney H (2007) Speech recognition techniques for a sign language recognition system. Interspeech, Antwerp, pp 2513–2516Google Scholar
  21. 21.
    Ong E-J et al (2012) Sign language recognition using sequential pattern trees. In: IEEE conference on computer vision and pattern recognition (CVPR) 2012. IEEE, pp 2200–2207Google Scholar
  22. 22.
    Agarwal A, Manish TK (2013) Sign language recognition using Microsoft Kinect. In: IEEE international conference on contemporary computing, 8–10 Aug 2013Google Scholar
  23. 23.
    Dieleman S, Kindermans P-J, Schrauwen B (2015) Sign language recognition using convolutional neural networks. In: ECCV 2014 workshops, part I, LNCS, vol 8925, pp 572–578Google Scholar
  24. 24.
    Garcia B (2015) Real-time American sign language recognition with convolutional neural networks. Stanford University, StanfordGoogle Scholar
  25. 25.
    Bobic V (2016) Hand gesture recognition using neural network based techniques. School of Electrical Engineering, University of BelgradeCrossRefGoogle Scholar
  26. 26.
    Sugahara M, Kruchinin SP (2001) Controlled not gate based on a two-layer system of the fractional quantum hall effect. Mod Phys Lett B 15:473–477ADSCrossRefGoogle Scholar
  27. 27.
    Kruchinin S, Nagao H, Aono S (2010) Modern aspect of superconductivity: theory of superconductivity. World Scientific, p 232. ISBN: 9814261602Google Scholar
  28. 28.
    PostgreSQL official web site. http://www.postgresql.org

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • S. Kondratiuk
    • 1
  • K. Kruchynin
    • 1
  • Iu. Krak
    • 1
  • S. Kruchinin
    • 2
  1. 1.Department of Theoretical CyberneticsTaras Shevchenko National University of KyivKyivUkraine
  2. 2.Bogolyubov Institute for Theoretical PhysicsKyivUkraine

Personalised recommendations