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相关概念视频

Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

483
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
483
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 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
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

709
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
709
Deconvolution01:20

Deconvolution

186
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
186
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

233
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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MPCNet:通过运动-抛物线补充网络进行压缩多视图视频恢复.

Chang Wu1, Gang He2, Xinquan Lai1

  • 1School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, China.

Neural networks : the official journal of the International Neural Network Society
|September 15, 2023
PubMed
概括

本研究引入了一种新的运动-抛物线补充网络 (MPCNet),以增强压缩多视图视频 (MVV) 恢复. MPCNet有效地利用时间和抛物线信息,显著提高视频质量并减少压缩工件.

关键词:
深度神经网络是一个神经网络.多视图视频编码多视图视频编码立体声信息 立体声信息视频压缩恢复恢复视频压缩恢复

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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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科学领域:

  • 计算机视觉 计算机视觉
  • 视频处理 视频处理
  • 机器学习 机器学习

背景情况:

  • 现有的基于学习的压缩多视频 (MVV) 恢复方法的性能有限.
  • 这些方法往往无法利用来自时间和位领域的全面信息.
  • MVV中的压缩工件源于复杂的框架间,框架内和视图间的引用错误.

研究的目的:

  • 提出一个高效的网络,以恢复压缩MVV的质量.
  • 有效地利用来自时间和抛物线领域的立体声信息.
  • 提高压缩MVV的表示能力和恢复性能.

主要方法:

  • 引入一个带有粗和细阶段的运动-抛物线补充网络 (MPCNet).
  • 来自多个领域的特征的相互补偿,以逐步聚合信息.
  • 开发基于注意力的特征过和调制模块 (AFFM) 以实现高效的特征融合和抑制误导信息.

主要成果:

  • MPCNet实现了平均PSNR增加1.978dB,MS-SSIM增加了0.0282.2,其中PSNR的平均增加为1.978dB.
  • 观察到BD率显著降低,平均为47.342%.
  • 高级视觉任务的改进,包括用于语义细分的mIoU (0.352) 和用于对象检测的mAP (51.71).

结论:

  • 与最先进的方法相比,MPCNet在恢复压缩的MVV方面表现出卓越的性能.
  • 拟议的网络有效地消除了压缩扭曲,提高了主观视频质量.
  • 这种方法为随后的高层次计算机视觉任务提供了好处.