Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Discrete Fourier Transform01:15

Discrete Fourier Transform

1.2K
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...
1.2K
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.4K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.4K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

896
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
896
Properties of DTFT II01:24

Properties of DTFT II

701
In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω.
701
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

1.2K
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...
1.2K
Properties of Fourier Transform I01:21

Properties of Fourier Transform I

858
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
858

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Twin SVM-Based Classification of Alzheimer's Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA.

Journal of healthcare engineering·2017
Same author

Edge-based intramode selection for depth-map coding in 3D-HEVC.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2014
Same author

Level set method with automatic selective local statistics for brain tumor segmentation in MR images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2013
See all related articles

Related Experiment Video

Updated: Apr 18, 2026

A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

802

2D discrete Fourier transform on sliding windows.

Chun-Su Park

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 14, 2015
    PubMed
    Summary

    A new 2D sliding Discrete Fourier Transform (DFT) algorithm offers faster frequency spectra analysis for digital signals. This efficient method accelerates 2D signal processing in computer vision and image analysis applications.

    Area of Science:

    • Digital Signal Processing
    • Image Processing
    • Computer Vision

    Background:

    • The Discrete Fourier Transform (DFT) is fundamental for analyzing digital signal frequency spectra.
    • Efficient computation of DFT on sliding windows is crucial for real-time applications.
    • Existing 2D DFT algorithms face computational challenges in sliding window scenarios.

    Purpose of the Study:

    • To propose a novel 2D sliding Discrete Fourier Transform (SDFT) algorithm.
    • To enable fast computation of DFT for 2D sliding windows.
    • To enhance the efficiency of 2D signal processing in computer vision and image analysis.

    Main Methods:

    • Development of a 2D sliding DFT (2D SDFT) algorithm.
    • Direct computation of current DFT bins from previous window's precalculated bins.

    More Related Videos

    Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
    06:37

    Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

    Published on: June 15, 2022

    4.3K
    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
    08:36

    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

    Published on: March 21, 2019

    7.8K

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    A Multimodal Wide-Field Fourier-Transform Raman Microscope
    06:48

    A Multimodal Wide-Field Fourier-Transform Raman Microscope

    Published on: December 30, 2025

    802
    Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
    06:37

    Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

    Published on: June 15, 2022

    4.3K
    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
    08:36

    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

    Published on: March 21, 2019

    7.8K
  • Theoretical analysis of computational complexity.

    • Main Results:

    • The proposed 2D SDFT algorithm achieves significant speed-up for 2D sliding window DFT.
    • It demonstrates the lowest computational requirement among existing 2D DFT algorithms.
    • The output of the 2D SDFT is mathematically equivalent to the traditional DFT.

    Conclusions:

    • The 2D SDFT algorithm provides an efficient solution for frequency spectra analysis in sliding 2D windows.
    • Its low computational cost makes it suitable for demanding computer vision and image processing tasks.
    • The algorithm offers a computationally superior and mathematically equivalent alternative to traditional 2D DFT methods.