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

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
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.

You might also read

Related Articles

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

Sort by
Same author

Towards Zero-Waste Furniture Design.

IEEE transactions on visualization and computer graphics·2016
Same author

Discovery of bicyclic pyrazoles as class III histone deacetylase SIRT1 and SIRT2 inhibitors.

Bioorganic & medicinal chemistry letters·2015
Same author

In vivo disassembly and reassembly of protein aggregates in Escherichia coli.

Journal of bacteriology·2014
Same author

Overall gloss evaluation in the presence of multiple cues to surface glossiness.

Journal of the Optical Society of America. A, Optics, image science, and vision·2012
Same author

Luminance-based specular gloss characterization.

Journal of the Optical Society of America. A, Optics, image science, and vision·2011
Same author

Meshless Animation of Fracturing Solids.

ACM transactions on graphics·2010

Related Experiment Video

Updated: Jun 7, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

Improving Gabor noise.

Ares Lagae1, Sylvain Lefebvre, Philip Dutré

  • 1Departement Computerwetenschappen, Katholieke Universiteit Leuven, Celestijnenlaan 200a—bus 2402, 3001 Heverlee, Belgium. ares.lagae@cs.kuleuven.be

IEEE Transactions on Visualization and Computer Graphics
|November 3, 2010
PubMed
Summary

We introduce Gabor noise, a novel procedural noise function. Improvements include an isotropic kernel for faster computation, error analysis, and spatially varying parameters, enhancing its utility.

Area of Science:

  • Computer Graphics
  • Procedural Noise Generation

Background:

  • Existing procedural noise functions lack a unique combination of properties.
  • Gabor noise was recently proposed as a novel alternative.

Purpose of the Study:

  • To present significant improvements to the Gabor noise function.
  • To enhance the performance and applicability of Gabor noise.

Main Methods:

  • Introduction of an isotropic kernel for Gabor noise.
  • Development of an error analysis for Gabor noise.
  • Implementation of spatially varying Gabor noise.

Main Results:

  • The isotropic kernel approximately doubles the speed of isotropic Gabor noise.
  • Error analysis quantifies the relationship between kernel truncation and relative error.

Related Experiment Videos

Last Updated: Jun 7, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

  • Spatially varying Gabor noise allows for dynamic parameter adjustment.

    • Conclusions:

    • The presented improvements make Gabor noise a more efficient and versatile tool.
    • Gabor noise offers a compelling alternative to existing procedural noise functions.