Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Transformations of Functions III

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...

Basic Operations on Signals

Basic Operations on Signals

Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Transformations of Functions II

Transformations of Functions II

Transformations in mathematics alter the position or orientation of a function’s graph while preserving its fundamental shape. One important type of transformation is the horizontal shift, which involves modifying the input variable within a function’s equation. This operation affects where outputs occur along the horizontal axis but does not alter the function’s overall structure.A horizontal shift is achieved by replacing the input variable x with either x + c or x - c, where c is a constant.

Transformation

Transformation

Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...

Continuous -time Fourier Transform

Continuous -time Fourier Transform

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...

You might also read

Related Articles

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

Sort by

Same author

An unsupervised texture segmentation algorithm with feature space reduction and knowledge feedback.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same author

Restoring visual perception using microsystem technologies: engineering and manufacturing perspectives.

Acta neurochirurgica. Supplement·2007

Same author

Image acquisition and image processing for the intraocular vision aid.

Biomedizinische Technik. Biomedical engineering·2002

Same author

Wireless power and data transmission system for a micro implantable intraocular vision aid.

Biomedizinische Technik. Biomedical engineering·2002

Same author

[Algorithm for automatic endocardium identification in digital echocardiography image sequences].

Biomedizinische Technik. Biomedical engineering·1998

Same author

[A flexible ECG compression algorithm based on wavelet transformation for use in transmission systems].

Biomedizinische Technik. Biomedical engineering·1997

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

Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Adaptive Gabor transformation for image processing.

A Teuner¹, B J Hosticka

¹Fraunhofer Inst. of Microelectron. Circuits and Syst., Duisburg.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1993

Summary

This summary is machine-generated.

This study introduces an adaptive filter for computing Gabor coefficients, demonstrating its stability for image compression. The Gabor transformation shows efficient information coding potential compared to discrete cosine transformation.

Related Experiment Videos

Last Updated: Jul 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Image processing
Signal processing
Data compression

Background:

Gabor transformation is a powerful tool for image analysis.
Efficient image compression is crucial for data storage and transmission.
Adaptive filters offer dynamic signal processing capabilities.

Purpose of the Study:

To present an algorithm for computing Gabor coefficients using an adaptive filter.
To analyze the numerical characteristics and stability of the proposed filter.
To investigate the efficiency of Gabor coefficients for image compression and compare it with Discrete Cosine Transformation (DCT).

Main Methods:

Development of an adaptive filter utilizing the complex least mean-square algorithm.
Numerical analysis of the filter's characteristics and stability conditions.
Comparative study of information coding efficiency between Gabor and DCT coefficients.

Main Results:

The proposed adaptive filter is stable under specific conditions.
Gabor transformation demonstrates significant potential for efficient image compression.
Gabor coefficients offer competitive or superior information coding efficiency compared to DCT.

Conclusions:

The adaptive filter provides a stable and efficient method for computing Gabor coefficients.
Gabor transformation is a viable and effective technique for image compression.
Further research into Gabor transformation for image compression is warranted.