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相关概念视频

Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

388
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
388
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

486
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...
486
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

378
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
378
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

355
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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
355
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

240
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...
240
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

421
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...
421

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用运动向量进行光学流量估计的测试时间调整.

Seyed Mehdi Ayyoubzadeh, Wentao Liu, Irina Kezele

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |August 31, 2023
    PubMed
    概括

    本研究介绍了以运动向量 (TTA-MV) 为指导的测试时间适应,以改进深度学习的光流估计. TTA-MV利用压缩视频中的运动向量来实现更好的现实世界的概括.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 用于光学流量估计的深度学习模型由于依赖合成训练数据,经常与现实数据作斗争.
    • 由于合成训练和现实世界测试环境之间的分配转移,性能下降发生.

    研究的目的:

    • 开发一种在测试时适应光流估计模型的方法,增强它们对现实场景的概括性.
    • 为应对高成本和技术困难所带来的挑战,注释现实世界的光流数据.

    主要方法:

    • 提出一个自我监督的学习任务,在测试时间调整光流模型.
    • 使用易于从压缩视频格式中获取的运动向量和残余.
    • 制定自主监督任务作为运动向量的预测,将其与光学流量估计联系起来.

    主要成果:

    • 拟议的以运动向量为指导的测试时间调整 (TTA-MV) 是第一个使用运动向量调整光流估计的系统.
    • TTA-MV显著提高了已建立的深度学习光流方法的概括能力.
    • 实验结果显示,在使用TTA-MV时,FlowNet,PWCNet和RAFT等模型的性能提高.

    结论:

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    • 在光学流量估计中,TTA-MV提供了一种有效的解决方案,可以弥合合成训练和现实世界的性能之间的差距.
    • 该方法提供了一种实用的方法,可以提高光流模型的稳定性,而不需要基本真相注释.
    • 这项工作为基于深度学习的更可靠,更广泛应用的光流解决方案铺平了道路.