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

Introduction to Vector Fields01:28

Introduction to Vector Fields

Vector fields provide a mathematical framework for describing quantities that possess both magnitude and direction at every point in space. Physical phenomena such as wind flow, ocean currents, magnetic forces, and fluid motion can all be represented using vector fields. In meteorology, for example, wind may vary continuously across a geographic region, with both speed and direction changing from one location to another. To visualize this behavior on a two-dimensional map, arrows are placed at...
Electric Field Lines01:25

Electric Field Lines

The three-dimensional representation of the electric field of a positive point charge requires tracing the electric field vectors, whose lengths decrease as the square of their distance from the charge and which point away from the charge at each point. This vector field is no doubt challenging to visualize. The visualization of electric fields becomes quickly intractable as the number of charges increases.
The solution to this problem is to use electric field lines, which are not vectors but...
Polar Coordinates: Problem Solving01:27

Polar Coordinates: Problem Solving

Directional radiation patterns are central to antenna analysis, as they illustrate how signal strength varies with direction. These patterns are often modeled using polar plots, where the radial distance from the origin represents signal intensity at a given angle. A commonly used idealized form is the four-lobed rose curve, which captures the concept of directional beams in a simplified mathematical form.The four-lobed rose curve, described by r = cos⁡(2θ), features four symmetric lobes, each...

You might also read

Related Articles

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

Sort by
Same author

Comparison of User Performance and Experience Between Light Field and Conventional AR Glasses.

IEEE transactions on visualization and computer graphics·2025
Same author

Generating Light Field From Stereo Images for AR Display With Matched Angular Sampling Structure and Minimal Retinal Error.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Quantitative assessment of <i>in vivo</i> nuclei and layers of human skin by deep learning-based OCT image segmentation.

Biomedical optics express·2025
Same author

Geometric lightguide for near-eye light field displays.

Applied optics·2024
Same author

Training With Uncertain Annotations for Semantic Segmentation of Basal Cell Carcinoma From Full-Field OCT Images.

IEEE transactions on medical imaging·2023
Same author

Toward cell nuclei precision between OCT and H&E images translation using signal-to-noise ratio cycle-consistency.

Computer methods and programs in biomedicine·2023
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

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

Semantic Frame Interpolation.

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

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

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

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

Related Experiment Video

Updated: Jun 10, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Light field analysis for modeling image formation.

Chia-Kai Liang1, Yi-Chang Shih, Homer H Chen

  • 1Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 5, 2010
PubMed
Summary
This summary is machine-generated.

This study unifies image formation models using light transport analysis. It represents images as 4-D light fields, simplifying complex optical effects into a single equation for better analysis.

More Related Videos

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

Related Experiment Videos

Last Updated: Jun 10, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

Area of Science:

  • Computer Vision
  • Optics
  • Computational Imaging

Background:

  • Traditional image formation models are fragmented, making aggregation of effects challenging.
  • Existing models struggle to integrate diverse optical phenomena cohesively.

Purpose of the Study:

  • To develop a unified mathematical framework for image formation.
  • To represent image formation using light transport analysis and 4-D light fields.
  • To elegantly describe the entire image formation process with a single equation.

Main Methods:

  • Applied light transport analysis to derive a unified model.
  • Represented radiance along light rays as a 4-D light field signal.
  • Modeled physical phenomena like refraction and blocking as linear transformations of the light field.

Main Results:

  • Developed a unified image formation model based on 4-D light fields.
  • Demonstrated that geometric and photometric effects (e.g., perspective, defocus, vignetting) can be represented within this framework.
  • Showed that the unified model's results match traditional models.

Conclusions:

  • The unified framework elegantly describes image formation with a single equation.
  • This approach simplifies the representation of complex optical phenomena.
  • The theoretical framework has potential for generalizations and diverse applications in imaging.