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Continuous -time Fourier Transform01:11

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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...
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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...
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Part-Wise Graph Fourier Learning for Skeleton-Based Continuous Sign Language Recognition.

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
Summary
This summary is machine-generated.

This study introduces a new part-wise graph Fourier learning method for sign language recognition, improving accuracy and reducing computational costs. The novel approach effectively models complex body part movements for better visual language understanding.

Keywords:
Fourier fully connected graphcontinuous sign language recognitionfrequency enhancementpart-wise action recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language recognition faces challenges with RGB inputs due to computational costs and noise interference.
  • Accurately modeling nonlinear temporal dynamics and asynchrony across body parts in sign language is difficult.

Purpose of the Study:

  • To propose a novel part-wise graph Fourier learning method (PGF-SLR) for skeleton-based continuous sign language recognition.
  • To address the limitations of existing methods by uniformly modeling spatiotemporal relations of body parts.

Main Methods:

  • Constructed a part-level Fourier fully connected graph treating body parts as nodes and frequency domain attention as edges.
  • Employed an adaptive frequency enhancement method to amplify discriminative action features.
  • Utilized a dual-branch action learning module with an auxiliary prediction branch for enhanced understanding.

Main Results:

  • Achieved relative improvements of 3.31%/3.70% and 2.81%/7.33% on PHOENIX14 and PHOENIX14-T datasets, respectively.
  • Demonstrated competitive performance on the CSL-Daily dataset, showing strong generalization.
  • Reduced computational costs in both offline and online settings.

Conclusions:

  • The proposed PGF-SLR method effectively captures spatiotemporal dependencies in the frequency domain for sign language recognition.
  • PGF-SLR offers a lightweight, robust, and computationally efficient solution for continuous sign language recognition.
  • The method shows significant potential for real-world applications requiring accurate and fast sign language understanding.