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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.

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Related Experiment Video

Updated: Oct 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Fully automatic image colorization based on semantic segmentation technology.

Min Xu¹, YouDong Ding¹

¹Shanghai Film Academy, Shanghai University, Shanghai, PR China.

|November 30, 2021

Summary

This summary is machine-generated.

This study proposes a novel deep learning model for automatic image colorization by optimizing semantic segmentation. The model effectively predicts realistic colors, overcoming issues like color bleeding in complex scenes.

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Last Updated: Oct 11, 2025

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Traditional deep learning image colorization methods suffer from color bleeding and insufficient color.
Existing algorithms often require manual intervention or extensive training data.

Purpose of the Study:

To develop a fully automatic image colorization model.
To address limitations of current deep learning colorization techniques by integrating semantic segmentation.
To achieve realistic and natural color prediction in diverse image scenarios.

Main Methods:

A novel approach reframing image colorization as semantic segmentation optimization.
Utilizing an encoder for local feature extraction and VGG-16 for global feature extraction, sharing low-level features.
Implementing two fusion modules to integrate local and global features for semantic segmentation and color prediction networks.

Main Results:

The proposed model demonstrates progressively stronger performance with increased data.
Successful prediction of reasonable and accurate colors, even in complex image scenes.
Generation of highly realistic and natural-looking colorized images.

Conclusions:

The integration of semantic segmentation significantly enhances image colorization capabilities.
The model offers a robust solution for automatic, high-quality image colorization.
The approach proves effective in producing natural and accurate chromatic predictions across various complexities.