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相关概念视频

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

404
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
404
Discrete Fourier Transform01:15

Discrete Fourier Transform

404
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
404
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

578
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
578
Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
7.0K
Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Fast Fourier Transform01:10

Fast Fourier Transform

465
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
465

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相关实验视频

Updated: Sep 10, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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基于骨架的连续手语识别的部分智能图形里埃学习

Dong Wei1, Hongxiang Hu1, Gang-Feng Ma1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

Journal of imaging
|August 27, 2025
PubMed
概括

这项研究引入了一种新的部分智能图形里埃学习方法,用于手语识别,提高准确性并降低计算成本. 这种新的方法有效地模拟了复杂的身体部位运动, 以更好地理解视觉语言.

科学领域:

  • 计算机科学
  • 人工智能
  • 人与计算机的交互

背景情况:

  • 由于计算成本和噪音干扰,手语识别面临RGB输入的挑战.
  • 在手语中准确地建模非线性时间动态和身体各部位的异步是很困难的.

研究的目的:

  • 为基于骨架的连续手语识别提出一个新的部分智能图形里埃学习方法 (PGF-SLR).
  • 通过对身体部位的时空关系进行统一的建模来解决现有方法的局限性.

主要方法:

  • 构建了一个部分水平的富里埃完全连接的图形,将身体部分视为节点,频域注意力视为边缘.
  • 采用适应频率增强方法来放大歧视性作用特征.
  • 使用双分支行动学习模块与辅助预测分支来增强理解.

主要成果:

  • 在PHOENIX14和PHOENIX14- T数据集中分别获得了3. 31%/ 3. 70%和2. 81%/ 7. 33%的相对改善.
  • 在CSL-Daily数据集上表现出竞争力,显示出强烈的概括性.
  • 在线和离线设置中降低计算成本.

结论:

  • 拟议的PGF-SLR方法有效地捕捉了信号语言识别频率域中的时空依赖性.
关键词:
福里埃完全连接图持续的手语识别频率增强部分行动认可

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  • PGF-SLR为连续的手语识别提供了一种轻量级,强大且计算效率高的解决方案.
  • 这种方法对于需要准确和快速的手语理解的现实应用具有显著的潜力.