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Related Concept Videos

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...
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.
UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent of conjugation in the...
Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
The essential components of a spectrophotometer include a source of electromagnetic radiation, a slot for placing a material to be analyzed, and a...
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)...
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...

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Related Experiment Video

Updated: May 20, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Color constancy with spatio-spectral statistics.

Ayan Chakrabarti1, Keigo Hirakawa, Todd Zickler

  • 1Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138, USA. ayanc@eecs.harvard.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 30, 2012
PubMed
Summary
This summary is machine-generated.

This study presents a new statistical method for color constancy, effectively removing color casts from images caused by lighting. The approach models color distributions to accurately estimate and correct illumination for improved image quality.

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Visualizing Visual Adaptation
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Related Experiment Videos

Last Updated: May 20, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Visualizing Visual Adaptation
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Published on: April 24, 2017

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Photography

Background:

  • Color cast in images is a common artifact caused by the spectral distribution of scene illuminants.
  • Accurate color constancy is crucial for reliable image analysis and interpretation.
  • Existing methods often struggle with complex illumination conditions.

Purpose of the Study:

  • To develop an efficient maximum likelihood approach for addressing the color cast problem in digital images.
  • To improve the accuracy of estimating dominant scene illuminants.
  • To provide a robust method for color correction in various imaging scenarios.

Main Methods:

  • Developed a statistical model for the spatial distribution of colors in white-balanced images.
  • Utilized spatial band-pass filters to reveal unimodal and symmetric color distributions.
  • Employed maximum likelihood estimation to infer illumination parameters based on the model.
  • Integrated statistical prior information about illuminants naturally into the estimation process.

Main Results:

  • The proposed statistical model effectively captures color distributions in white-balanced images.
  • Spatial filtering reveals color distributions that are well-represented by a simple parametric form.
  • The maximum likelihood approach enables efficient estimation of dominant illuminants.
  • Experimental evaluations on standard datasets demonstrate the approach's strong performance.

Conclusions:

  • The developed method offers an efficient and effective solution for the color constancy problem.
  • The statistical modeling of color distributions provides a robust foundation for illumination estimation.
  • This approach has the potential to significantly enhance image quality and reliability in computer vision applications.