Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Implementation of an Autostereoscopic Virtual 3D Button in Non-contact Manner Using Simple Deep Learning Network

  • Received : 2020.09.15
  • Accepted : 2021.03.05
  • Published : 2021.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This research presented an implementation of autostereoscopic virtual three-dimensional (3D) button device as non-contact style. The proposed device has several characteristics about visible feature, non-contact use and artificial intelligence (AI) engine. The device was designed to be contactless to prevent virus contamination and consists of 3D buttons in a virtual stereoscopic view. To specify the button pressed virtually by fingertip pointing, a simple deep learning network having two stages without convolution filters was designed. As confirmed in the experiment, if the input data composition is clearly designed, the deep learning network does not need to be configured so complexly. As the results of testing and evaluation by the certification institute, the proposed button device shows high reliability and stability.

Keywords

References

  1. S. H. You, M. Hwang, K. H. Kim, and C. S. Cho, "Development of a non-contact autostereoscopic 3D button using artificial intelligence," in Advanced Multimedia and Ubiquitous Engineering. Singapore: Springer, 2021, pp. 69-76.
  2. Y. Oishi and Y. Yamashiro, "Control circuit of electrostatic capacitive sensor and electronic device using the same," U.S. Patent 9838527, Dec 5, 2017.
  3. S. Mondal, S. J. Kim, and C. G. Choi, "Honeycomb-like MoS2 nanotube array-based wearable sensors for noninvasive detection of human skin moisture," ACS Applied Materials & Interfaces, vol. 12, no. 14, pp. 17029-17038, 2020. https://doi.org/10.1021/acsami.9b22915
  4. N. Zengeler, T. Kopinski, and U. Handmann, "Hand gesture recognition in automotive human-machine interaction using depth cameras," Sensors, vol. 19, no. 1, article no. 59, 2019. https://doi.org/10.3390/s19010059
  5. Y. S. Jeong and J. H. Park, "Advanced big data analysis, artificial intelligence & communication systems," Journal of Information Processing Systems, vol. 15, no. 1, pp. 1-6, 2019. https://doi.org/10.3745/JIPS.02.0107
  6. S. Park, S. Cho, J. Park, K. Huang, Y. Sung, and K. Cho, "Infrared bundle adjusting and clustering method for head-mounted display and Leap Motion calibration," Human-centric Computing and Information Sciences, vol. 9, article no. 8, 2019. https://doi.org/10.1186/s13673-019-0169-6
  7. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 25, pp. 1097-1105, 2012.
  8. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, 2015.
  9. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 770-778.
  10. M. T. N. Truong and S. Kim, "A tracking-by-detection system for pedestrian tracking using deep learning technique and color information," Journal of Information Processing Systems, vol. 15, no. 4, pp. 1017-1028, 2019. https://doi.org/10.3745/JIPS.04.0132
  11. W. Song, L. Zhang, Y. Tian, S. Fong, J. Liu, and A. Gozho, "CNN-based 3D object classification using Hough space of LiDAR point clouds," Human-centric Computing and Information Sciences, vol. 10, article no. 19, 2020. https://doi.org/10.1186/s13673-020-00228-8
  12. D. Cao, Z. Chen, and L. Gao, "An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks," Human-centric Computing and Information Sciences, vol. 10, article no. 14, 2020. https://doi.org/10.1186/s13673-020-00219-9
  13. N. A. Dodgson, "Autostereoscopic 3D displays," Computer, vol. 38, no. 8, pp. 31-36, 2005. https://doi.org/10.1109/MC.2005.252
  14. C. W. Tyler and M. B. Clarke, "Autostereogram," in Proceedings of SPIE 1256: Stereoscopic Displays and Applications. Bellingham, WA: International Society for Optics and Photonics, 1990, pp. 182-197.
  15. D. Ezra, G. J. Woodgate, B. A. Omar, N. S. Holliman, J. Harrold, and L. S. Shapiro, "New autostereoscopic display system," in Proceedings of SPIE 2409: Stereoscopic Displays and Virtual Reality Systems II. Bellingham, WA: International Society for Optics and Photonics, 1995, pp. 31-40.
  16. P. Bourke, "Autostereoscopic lenticular images," 2014 [Online]. Available: http://paulbourke.net/stereographics/lenticular/.
  17. D. P. Kingma and J. Ba, "Adam: a method for stochastic optimization," in Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, 2015.