Jove
Visualize
联系我们
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

相关概念视频

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the drone...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

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

A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals.

Scientific data·2026
Same author

Parameter-Efficient Adaptation of Large Vision-Language Models for Video Memorability Prediction.

Sensors (Basel, Switzerland)·2025
Same author

Dual Leap Motion Controller 2: A Robust Dataset for Multi-view Hand Pose Recognition.

Scientific data·2024
Same author

A dataset of synthetic art dialogues with ChatGPT.

Scientific data·2024
Same author

Sign Language Motion Generation from Sign Characteristics.

Sensors (Basel, Switzerland)·2023
Same author

Reducing the Impact of Sensor Orientation Variability in Human Activity Recognition Using a Consistent Reference System.

Sensors (Basel, Switzerland)·2023
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
查看所有相关文章

相关实验视频

Updated: Jun 8, 2026

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
12:43

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Published on: February 21, 2011

34.8K

标志语言数据集用于自动运动生成.

María Villa-Monedero1, Manuel Gil-Martín1, Daniel Sáez-Trigueros2

  • 1Grupo de Tecnología del Habla y Aprendizaje Automático (T.H.A.U. Group), Information Processing and Telecommunications Center, E.T.S.I. de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Journal of imaging
|December 22, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的手语数据集,包含语音和运动数据,用于自动生成手语. 初步结果显示,符号语音的动作生成能力是有希望的.

关键词:
汉姆没有Sysys.标志提取地标提取地标运动数据集 运动数据集标语是指手语的使用方式.标志 语音符号 语音符号

更多相关视频

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication
07:18

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Published on: January 26, 2024

905
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

500

相关实验视频

Last Updated: Jun 8, 2026

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
12:43

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Published on: February 21, 2011

34.8K
Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication
07:18

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Published on: January 26, 2024

905
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

500

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 语言学的语言学.

背景情况:

  • 现有的手语数据集主要集中在识别和翻译上.
  • 需要为自动手语运动生成量身定制的数据集.

研究的目的:

  • 引入用于自动运动生成的新型手语数据集.
  • 提供语音注释 (HamNoSys) 和详细的运动数据 (视频,地标) 一套全面的标志.

主要方法:

  • 数据集包括754个西班牙手语 (LSE) 符号,包括字母表,数字和常用短语.
  • 运动数据包括6786个视频,其中使用MediaPipe提取了骨地标 (面部,身体,手).
  • 每个视频的标志都用HamNoSys的音符进行注释.

主要成果:

  • 该数据集有助于培训系统从音符中生成手语运动.
  • 初步的运动生成实验实现了每0.37的动态时间扭曲距离.

结论:

  • 本文所介绍的数据集是推动自动手语动作生成研究的宝贵资源.
  • 这些发现表明,从语音表示表现生成手语运动的可行性.