Open Access
SHS Web Conf.
Volume 144, 2022
2022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)
Article Number 03011
Number of page(s) 5
Section Application of Artificial Intelligence Technology and Machine Learning Algorithms
Published online 26 August 2022
  1. Lai, K., & Yanushkevich, S. N. (2018). CNN+RNN depth and skeleton based dynamic hand gesture recognition. 2018 24th International Conference on Pattern Recognition (ICPR). [Google Scholar]
  2. He, X., & Zhang, J. (2020). Design and implementation of number gesture recognition system based on Kinect. 2020 39th Chinese Control Conference (CCC). [Google Scholar]
  3. Yang, F., Sun, Q., Jin, H., & Zhou, Z. (2020). Superpixel segmentation with fully Convolutional networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). [Google Scholar]
  4. De Oliveira Junior, L. A., Medeiros, H. R., Macedo, D., Zanchettin, C., Oliveira, A. L., & Ludermir, T. (2018). SegNetRes-CRF: A deep Convolutional encoder-decoder architecture for semantic image segmentation. 2018 International Joint Conference on Neural Networks (IJCNN). [Google Scholar]
  5. Reyes, M., Dominguez, G., & Escalera, S. (2011). Featureweighting in dynamic timewarping for gesture recognition in depth data. 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). [Google Scholar]
  6. Simo-Serra, E., Ramisa, A., Alenya, G., Torras, C., & Moreno-Noguer, F. (2012). Single image 3D human pose estimation from noisy observations. 2012 IEEE Conference on Computer Vision and Pattern Recognition. [Google Scholar]
  7. Sinha, A., Choi, C., & Ramani, K. (2016). DeepHand: Robust hand pose estimation by completing a matrix imputed with deep features. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [Google Scholar]
  8. Zhang, X., Wang, J., Wang, X., & Ma, X. (2016). Improvement of dynamic hand gesture recognition based on HMM algorithm. 2016 International Conference on Information System and Artificial Intelligence (ISAI). [Google Scholar]
  9. Cicirelli, G., & D’Orazio, T. (2017). Gesture recognition by using depth data: Comparison of different methodologies. Motion Tracking and Gesture Recognition. [Google Scholar]
  10. Cui, H., & Wang, Y. (2020). Research on gesture recognition method based on computer vision technology. 2020 International Conference on Computer Information and Big Data Applications (CIBDA). [Google Scholar]
  11. Zhao, D., Liu, Y., & Li, G. (2018). Skeleton-based dynamic hand gesture recognition using 3D depth data. Electronic Imaging, 30(18), 4611-4618. [Google Scholar]
  12. Kumar, V., Namboodiri, A., Paluri, M., & Jawahar, C. V. (2017). Pose-aware person recognition. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [Google Scholar]
  13. Martínez-Hinarejos, C., & Parcheta, Z. (2017). Spanish sign language recognition with different topology hidden Markov models. Interspeech 2017. [Google Scholar]
  14. Zhang, C., & Tian, Y. (2013). Edge enhanced depth motion map for dynamic hand gesture recognition. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. [Google Scholar]
  15. Zhang, Q., & Deng, F. (2017). Dynamic gesture recognition based on LeapMotion and HMM-CART model. Journal of Physics: Conference Series, 910, 012037. [CrossRef] [Google Scholar]

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