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

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...
Color Vision01:24

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.

You might also read

Related Articles

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

Sort by
Same author

Unsupervised Domain Adaptive Object Detection via Semantic Consistency and Compactness Learning.

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

Retraction notice to "Research on the construction of a collaborative ability evaluation system for the joint graduation design of new engineering specialty groups based on digital technology" [Heliyon 9 (2023) e16855].

Heliyon·2026
Same author

MGAF: LiDAR-Camera 3D Object Detection With Multiple Guidance and Adaptive Fusion.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Interaction-Aware Transformer Network for Human-Object Interaction Detection.

IEEE transactions on cybernetics·2025
Same author

Learning a Non-Locally Regularized Convolutional Sparse Representation for Joint Chromatic and Polarimetric Demosaicking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

A Novel Image Formation Model for Descattering.

IEEE transactions on pattern analysis and machine intelligence·2024
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
Same journal

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

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

Related Experiment Video

Updated: Jun 21, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Tricolor attenuation model for shadow detection.

Jiandong Tian1, Jing Sun, Yandong Tang

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China. tianjd@sia.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 10, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for shadow extraction from single outdoor images using a tricolor attenuation model (TAM). The model accurately identifies shadows in complex scenes without needing prior information.

Related Experiment Videos

Last Updated: Jun 21, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Photography

Background:

  • Shadows are prevalent in outdoor scenes, posing challenges for image analysis.
  • Existing shadow detection methods often require multiple images or specific scene conditions.

Purpose of the Study:

  • To develop a novel method for extracting shadows from single outdoor images.
  • To address limitations of previous shadow detection techniques.

Main Methods:

  • Derivation of a tricolor attenuation model (TAM) based on image formation theory.
  • Estimation of TAM parameters using spectral power distribution (SPD) of daylight and skylight, based on Planck's law.
  • Implementation of a multistep shadow detection algorithm utilizing the TAM.

Main Results:

  • The proposed algorithm effectively extracts shadows from single, complex outdoor images.
  • The method operates without requiring prior knowledge of the scene.
  • Experimental results demonstrate the model's robust performance.

Conclusions:

  • The tricolor attenuation model provides a robust framework for shadow extraction.
  • The developed algorithm offers a practical solution for shadow detection in real-world scenarios.
  • This approach advances single-image shadow removal capabilities.