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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

You might also read

Related Articles

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

Sort by
Same author

Large-scale single-molecule analysis of tau proteoforms.

bioRxiv : the preprint server for biology·2025
Same author

Ampoules of injectable tranexamic acid are unusable after freezing.

BMJ military health·2024
Same author

The musculoskeletal manifestations of haemophilia: a review of the imaging findings.

Clinical radiology·2022
Same author

New insights regarding origin of monosomy occurrence in early developing embryos as demonstrated in preimplantation genetic testing.

Molecular cytogenetics·2022
Same author

Clinical anatomy of the nerve supply to the upper limb.

BJA education·2021
Same author

Variables associated with survival in patients with invasive bladder cancer with and without surgery.

Anaesthesia·2020
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Multiple resolution texture analysis and classification.

S Peleg1, J Naor, R Hartley

  • 1Center for Automation Research, University of Maryland, College Park, MD 20742; Department of Computer Science, the Hebrew University of Jerusalem, 91904 Jerusalem, Israel.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

Texture classification uses fractal properties derived from how surface area changes with image resolution. This method quanties texture details for comparison and categorization, offering insights into image characteristics.

More Related Videos

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Related Experiment Videos

Last Updated: May 29, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Area of Science:

  • Computer vision
  • Image analysis
  • Fractal geometry

Background:

  • Texture analysis is crucial for image understanding.
  • Traditional methods often struggle with scale-dependent details.
  • Quantifying texture properties requires robust metrics.

Purpose of the Study:

  • To classify textures based on their scale-dependent properties.
  • To introduce a method using fractal dimension for texture analysis.
  • To explore directional properties and negative image relations in textures.

Main Methods:

  • Measuring the gray level surface area at multiple resolutions.
  • Calculating fractal properties from the rate of area decrease with coarser resolutions.
  • Analyzing texture comparison, classification, and directional attributes.

Main Results:

  • Texture properties change predictably with resolution.
  • Fractal dimension quantifies the rate of detail loss.
  • This approach enables effective texture classification and comparison.

Conclusions:

  • Fractal analysis of resolution-dependent area changes provides a robust method for texture classification.
  • The technique captures essential texture characteristics, including directional information.
  • This framework enhances image analysis by quantifying scale-invariant texture features.