Abstract
The approach to the development of informational computer technologies for the interactive learning of sign language which use 3D model human expart of sign language is proposed. Methods for modeling fingerspelling information and it’s recognizing uses convolution neural networks are created. The implementation of these methods under the operating system Microsoft Windows and on the cross-platform technology have been developed. Methods and software for gestures modeling base on 3D human model and using Motion Capture technology are created.
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Brentari, D. (ed.): Sing Languages. Cambridge University Press, Cambridge (2010)
Stokoe Jr., W.C.: Sing language structure: an outline of the visual communication systems of the American deaf. University of Buffalo (1960)
Smith, R., Morrissey, S., Somers, H.: HCI for the deaf community: developing human-like avatars for sign language synthesis. In: Proceedings of the 4th Irish Human Computer Interaction Conference, Dublin, pp. 129–136 (2010)
Kryvonos, Iu.G., Krak, Yu.V., Barchukova, Yu.V., Trocenko, B.A.: Human hand motion parametrization for dactylemes modeling. J. Autom. Inf. Sci. 43(12), 1–11 (2011)
Krak, Iu., Kryvonos, Iu., Wojcik, W.: Interactive sytems for sign language learning. In: 6th International Conference on Application of Information and Communication Technologies (AICT), pp. 114–116 (2012)
Kondratiuk, S., Krak, Iu.: Dactyl alphabet modeling and recognition using cross platform software. In: IEEE Second International Conference on Data Stream Minning & Processing (DSMP), pp. 420–423 (2018)
Unity3D framework. [Electronic resource] www.unity3d.com. Accessed 20 Apr 2018
Tensorflow framework documentation. [Electronic resource] www.tensorflow.org/api/. Accessed 20 Apr 2018
Ong, E.-J., et al.: Sign language recognition using sequential pattern trees. In: IEEE Conference on IEEE Computer Vision and Pattern Recognition (CVPR), pp. 2200–2207 (2012)
Kryvonos, I.G., Krak, I.V., Barmak, O.V., Shkilniuk, D.V.: Construction and identification of elements of sign communication. Cybern. Syst. Anal. 49(2), 163–172 (2013)
Krak, Yu.V., Golik, A.A., Kasianiuk, V.S.: Recognition of dactylemes of Ukrainian sign language based on the geometric characteristics of hand contours defects. J. Autom. Inf. Sci. 48(12), 1–11 (2011)
Menache, A.: Understanding Motion Capture for Computer Animation and Video Games. Morgan Kaufmann, Burlington (2000)
Avidan, S.: Support vector tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 1, Kauai, Hawaii, pp. 84–191 (2001)
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Krak, I. (2019). Computer Technologies for Gestures Communication Systems Construction. In: Shokin, Y., Shaimardanov, Z. (eds) Computational and Information Technologies in Science, Engineering and Education. CITech 2018. Communications in Computer and Information Science, vol 998. Springer, Cham. https://doi.org/10.1007/978-3-030-12203-4_13
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DOI: https://doi.org/10.1007/978-3-030-12203-4_13
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