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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.
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Perceptual Constancy01:12

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
UV–Vis Spectrum01:30

UV–Vis Spectrum

When light passes through a substance, a portion of the light is absorbed while the remaining light is reflected or transmitted. If the molecule absorbs light between the wavelengths of 180–400 nm range, the UV spectrum is obtained, and if it absorbs light in the 400–780 nm wavelength range, the visible spectrum is obtained.     
The UV–Vis spectrum of a molecule is the plot of its absorbance versus wavelength. The plot is drawn by taking molar absorptivity (ε) or log ε on the y-axis (ordinate)...
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for electronic transitions. As a result...
Coefficient of Variation01:10

Coefficient of Variation

The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...

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Related Experiment Video

Updated: May 25, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

A chromatic diversity index based on complex scenes.

João Manuel Maciel Linhares1, Sérgio Miguel Cardoso Nascimento

  • 1Anglia Ruskin University, Faculty of Science and Technology, East Road, COS204, Cambridge CB1 1PT, UK. joao.linhares@anglia.ac.uk

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|February 15, 2012
PubMed
Summary

A new chromatic diversity index, using the Munsell set, accurately predicts how complex scenes change color under different lighting conditions. This index shows strong correlation, validating its use for predicting illuminant-induced chromatic shifts.

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Area of Science:

  • Color Science
  • Image Processing

Background:

  • Chromatic diversity is crucial for understanding complex scenes.
  • Predicting color changes under varying illuminants is challenging.

Purpose of the Study:

  • To develop a chromatic diversity index based on the Munsell set.
  • To evaluate its ability to predict illuminant-induced color changes in complex scenes.

Main Methods:

  • Hyperspectral data of complex scenes were analyzed under test and reference CIE D65 illuminants.
  • Color differences were computed for scenes and Munsell samples.
  • Correlation analysis was performed between scene and Munsell color differences.

Main Results:

  • A high average correlation (approx. 0.94) was found between color differences in complex scenes and the Munsell set.
  • The proposed index effectively predicts illuminant-induced chromatic diversity changes.

Conclusions:

  • The Munsell set provides a reliable basis for a chromatic diversity index.
  • This index can accurately predict chromatic changes in complex scenes under different illuminants.