Artificial intelligence

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 using trained on collected training dataset set convolutional neural network. 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, dactyl recognition, convolutional neural networks

References:

1. Peter Mell and Timothy Grance (September 2011). The NIST Definition of Cloud Computing (Technical report). National Institute of Standardsand Technology: U.S. Department of Commerce. doi:10.6028/NIST.SP.800-145
2. The Linux Information Project, Cross-platform Definition.
3. Smith, James; Nair, Ravi (2005). "The Architectureof Virtual Machines". Computer. IEEE Computer Society. 38 (5): 32–38.
4. I. Krak, S. Kondratiuk (2017). Cross-platform software for the development of sign communication system: Dactyl language modelling, Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT2017, 1, pp. 167-170. DOI: 10.1109/STC-CSIT.2017.8098760
5. Yu.V. Krak, Yu.V. Barchukova, B.A. Trotsenko(2011). Human hand motion parametrization for dactylemes modeling, Journal of Automation and Information Sciences, 43 (12), pp. 1-11.
6. ASL Sing language dictionary http://www.signasl.org/sign/model
7. Apple Touchless Gesture System for iDevices http://www.patentlyapple.com/patently-apple/2014/12/apple-invents-a-highly-advanced-air-gesturing-system-for-future-idevices-and-beyond.html
8. Comparative study of hand gesture recognition system, Rafiqul Zaman Khan, Noor Adnan Ibraheem, Natarajan Meghanathan, et al. (Eds): SIPM, FCST, ITCA, WSE, ACSIT, CS & IT 06, pp. 203–213, 2012.
9. Gesture Modeling and Animation by Imitation, Michael Neff, Michael Kipp, Irene Albrecht, Hans-Peter Seidel, MPI–I–2006–4-008 September 2006.
10. Dynamic Controller Toolkit, Ari Shapiro, Derek Chu, Brian Allen, Petros Faloutsos, 2005. http://www.arishapiro.com/Sandbox07_DynamicToolkit.pdf
11. Iu.G. Kryvonos, Iu.V. Krak, 2011: Modeling human hand movements, facial expressions, and articulation to synthesize and visualize gesture information, Cybernetics and Systems Analysis, 47 (4), pp. 501-505.
12. Iu.G. Kryvonos, Iu.V. Krak, O.V. Barmak, D.V. Shkilniuk, 2013: Construction and identification of elements of sign communication, Cybernetics and Systems Analysis, 49 (2), pp. 163-172.
13. Android based portable hand sign recognition system, Jagdish L. Raheja, March 2015. DOI: 10.15579/gcsr.vol3.ch1
14. Unity3D framework https://unity3d.com/
15. Tensorflow framework documentation https://www.tensorflow.org/api/
16. YAML – The Official YAML Web Site http://yaml.org/
17. Eng-Jon Ong et al. Sign language recognition using sequential pattern trees. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE. 2012, pp. 2200–2207.
18. American Sign language: Real-time American Sign Language Recognition with Convolutional Neural Networks Brandon Garcia Stanford University Stanford, CA, 2015.
19. Hand gesture recognition using neural network based techniques, Vladislav Bobic, School of Electrical Engineering, University of Belgrade, 2016.
20. Andrew G. Howard, Menglong Zhu, Bo Chen. “MobileNets: Efficient Convolutional NeuralNetworks for Mobile Vision Application”, 2017.
21. PostgreSQL official web site https://www.postgresql.org/