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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Discrete Fourier Transform01:15

Discrete Fourier Transform

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...
Parseval's Theorem for Fourier transform01:15

Parseval's Theorem for Fourier transform

Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a signal's...
Wave Parameters01:10

Wave Parameters

The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
Properties of Fourier Transform I01:21

Properties of Fourier Transform I

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...

You might also read

Related Articles

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

Sort by
Same author

Systematic review on machine learning applications in Paralympic sports: current practice and future research.

Disability and rehabilitation. Assistive technology·2026
Same author

Physically grounded deep learning-enabled gold nanoparticle localization and quantification in photonic resonator absorption microscopy for digital resolution molecular diagnostics.

Biosensors & bioelectronics·2025
Same author

Smartphone-Based Digitized Neurological Examination Toolbox for Multi-test Neurological Abnormality Detection and Documentation.

IEEE journal of biomedical and health informatics·2024
Same author

Structural characterization of lateral phase separation in polymer-lipid hybrid membranes.

Methods in enzymology·2024
Same author

Modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction.

Artificial intelligence in medicine·2024
Same author

Efficient Human Vision Inspired Action Recognition Using Adaptive Spatiotemporal Sampling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same journal

SinColor: Uncertainty-Guided Single-Step Diffusion for Image Colorization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Videos

Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance.

Minh N Do1, Martin Vetterli

  • 1Audio-Visual Communications Laboratory, Department of Communication Systems, Swiss Federal Institute of Technology, Lausanne, Switzerland. minhdo@uiuc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

A new statistical method for texture retrieval improves accuracy by jointly modeling feature extraction and similarity measurement. This wavelet-based approach enhances texture identification rates significantly compared to traditional methods.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Texture retrieval relies on feature extraction and similarity measurement.
  • Existing methods often model these tasks separately, limiting optimal performance.

Purpose of the Study:

  • To develop a unified statistical framework for texture retrieval.
  • To introduce a novel wavelet-based method for enhanced texture analysis and retrieval.

Main Methods:

  • Joint modeling of feature extraction and similarity measurement using a consistent estimator.
  • Utilizing Kullback-Leibler distance (KLD) for comparing generalized Gaussian density (GGD) models of wavelet coefficients.
  • Developing a wavelet-based texture retrieval algorithm based on GGD modeling.

Main Results:

  • The proposed joint modeling approach is asymptotically optimal for retrieval error probability.
  • The wavelet-based method significantly improves texture retrieval rates from 65% to 77% on a 640-image database.
  • The method offers greater accuracy and flexibility in capturing texture information.

Conclusions:

  • A unified statistical framework enhances texture retrieval performance.
  • The GGD-based wavelet method provides a statistically sound and effective approach to texture analysis.
  • This method offers a significant improvement over traditional texture identification techniques.