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相关概念视频

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

865
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...
865
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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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...
685
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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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...
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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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基于来自单个自我中心深度图像的回归的手对象姿势估计.

Jingang Lin1, Dongnian Li1, Chengjun Chen1

  • 1School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括

本研究引入了一种回归方法,用于从深度图像中准确地估计手-物体姿势. 计算机视觉系统有效地解决了闭塞挑战,提高了人类互动理解.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 人与计算机的交互

背景情况:

  • 准确的手对象姿势估计对于理解人类互动至关重要.
  • 严重的闭塞在基于视觉的姿势估计任务中是一个重大挑战.
  • 现有的方法在混乱或封闭的场景中难以准确地确定姿势.

研究的目的:

  • 开发一种强大的手-物体姿势估计方法,能够处理阻塞.
  • 为了同时估计手和操纵对象的3D姿势.
  • 提高计算机视觉系统在分析人与物体交互时的准确性和可靠性.

主要方法:

  • 使用3D中心检测方法从深度图像中提取前景手对象信息.
  • 在一个单一的框架内使用回归方法来同时估计姿势.
  • 用ResNet-50骨干实现一个卷积神经网络 (CNN) 进行关键点预测.

主要成果:

  • 拟议的方法在FPHA-HO数据集上实现了手的平均关键点误差11.85毫米,物体的平均误差18.97毫米.
  • 回归技术有效地估计了尽管阻塞的姿势.
  • 使用单个自我中心的深度图像证明了准确的姿势估计.
关键词:
回归的杆回归卷积神经网络是一种卷积神经网络.图像的深度图像的深度图像.手的姿势估计手的姿势估计.对象的姿势估计对象的姿势估计

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结论:

  • 开发的回归方法提供了准确的手对象姿势估计,即使有显著的阻塞.
  • 这一进步提高了计算机视觉系统的功能,用于详细分析人与物体的相互作用.
  • 该方法显示了需要精确理解操纵任务的应用程序的潜力.