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

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Color Vision

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

An AI system to help scientists write expert-level empirical software.

Nature·2026

Same author

Expert evaluation of LLM world models: A high-T<sub><i>c</i></sub> superconductivity case study.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Using large language models to accelerate communication for eye gaze typing users with ALS.

Nature communications·2024

Same author

Managing extreme AI risks amid rapid progress.

Science (New York, N.Y.)·2024

Same author

Real-world humanoid locomotion with reinforcement learning.

Science robotics·2024

Same author

Video Frame Interpolation With Many-to-Many Splatting and Spatial Selective Refinement.

IEEE transactions on pattern analysis and machine intelligence·2023

Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Long-Term Recurrent Convolutional Networks for Visual Recognition and Description.

Jeff Donahue¹, Lisa Anne Hendricks¹, Marcus Rohrbach¹

¹Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 9, 2016

Summary

This summary is machine-generated.

Recurrent convolutional models enhance sequence understanding by learning spatial and temporal features. These doubly deep networks improve tasks like activity recognition and image captioning.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Mar 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Artificial Intelligence
Computer Vision
Machine Learning

Background:

Deep convolutional networks excel at image interpretation.
Sequential data processing often uses fixed representations or temporal averaging.
Recurrent models offer potential for complex temporal dynamics.

Purpose of the Study:

Investigate the effectiveness of recurrent convolutional architectures for sequential tasks.
Develop end-to-end trainable models for large-scale visual understanding.
Demonstrate the value of these models for activity recognition, image captioning, and video description.

Main Methods:

Described a class of recurrent convolutional architectures.
Incorporated nonlinearities into network state updates for long-term dependencies.
Jointly trained recurrent sequence models with modern visual convolutional networks.

Main Results:

Recurrent convolutional models learn compositional representations in space and time.
These models can map variable-length inputs to variable-length outputs.
Demonstrated advantages over state-of-the-art models for recognition and generation tasks.

Conclusions:

Recurrent convolutional models offer a powerful approach for sequential visual understanding.
These models effectively capture complex temporal dynamics and perceptual representations.
Joint training enables superior performance in tasks like activity recognition and video description.