Jove
Visualize
联系我们

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Neural Residual Correction for 3D Tooth Point Cloud Canonicalization.

Journal of imaging·2026
Same author

A latent variable deep generative model for 3D anterior tooth shape.

Journal of prosthodontics : official journal of the American College of Prosthodontists·2025
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 23, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

通过ResNet和LSTM网络进行基于视频的手语识别.

Jiayu Huang1, Varin Chouvatut1

  • 1Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand.

Journal of imaging
|June 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用残余网络 (ResNet) 和长短期内存 (LSTM) 的新手语识别方法,以提高通信可访问性. 该ResNet-LSTM模型增强了时空特征提取,从而提高了识别手语操作的准确性.

关键词:
这是LSTM的LSTM.这就是ResNet ResNet.深度学习是一种深度学习.标志语言识别 标志语言识别

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jun 23, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 手语识别技术有助于听力障碍者进行沟通.
  • 深度学习为手语识别提供技术支持.
  • 使用卷积神经网络的传统方法在特征提取和计算资源方面面临挑战.

研究的目的:

  • 介绍一种新的基于视频的手语识别方法.
  • 为了提高手语识别的准确性和效率.
  • 解决传统方法在特征提取和计算需求方面的局限性.

主要方法:

  • 使用剩余网络 (ResNet) 作为特征提取的骨干.
  • 采用长短期记忆 (LSTM) 网络来捕获长序列特征.
  • 联合ResNet用于空间特征提取和LSTM用于从手语视频中学习时间特征.

主要成果:

  • 拟议的ResNet-LSTM方法在阿根廷手语 (LSA64) 数据集上实现了86.25%的准确性.
  • 证明了卓越的性能,F1得分为84.98%和精度为87.77%.
  • 有效地提取了时空特征,显著提高了手语动作识别率.

结论:

  • 该ResNet-LSTM方法为基于视频的手语识别提供了有效的解决方案.
  • 该方法增强了时空特征的提取,从而提高了识别精度.
  • 这项技术有望为听力受损社区提供更好的沟通工具.