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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.
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Related Experiment Video

Updated: Jun 2, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Extending "color constancy" outside the visible region.

Sivalogeswaran Ratnasingam1, Steve Collins, Javier Hernández-Andrés

  • 1Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, UK. siva@robots.ox.ac.uk

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|April 12, 2011
PubMed
Summary

This study extends color constancy algorithms to near-ultraviolet (UV) and near-infrared (IR) wavelengths, successfully extracting illuminant-invariant reflectance features. The modified algorithm shows promise for UV and IR data, enhancing material analysis capabilities.

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Area of Science:

  • Optics and Photonics
  • Computer Vision
  • Materials Science

Background:

  • Color constancy aims to achieve illuminant-invariant surface reflectance estimation.
  • Existing algorithms primarily focus on the visible light spectrum.
  • Extending these algorithms to broader wavelength regions like UV and IR is crucial for comprehensive material characterization.

Purpose of the Study:

  • To investigate the extension of a blackbody-model-based color constancy algorithm to near-ultraviolet (UV) and near-infrared (IR) wavelength regions.
  • To extract two illuminant-invariant reflectance features from sensor data covering visible and either near-UV or near-IR wavelengths.
  • To validate the use of the goodness-fitness coefficient (GFC) for generating test datasets.

Main Methods:

  • Extended a blackbody-model-based color constancy algorithm to incorporate near-UV and near-IR spectral data.
  • Generated test reflectance datasets using the goodness-fitness coefficient (GFC) and compared with CIELab distance-generated data.
  • Modified the algorithm to accommodate non-blackbody spectral characteristics of daylight in the near-UV region.

Main Results:

  • Demonstrated the successful extraction of two illuminant-invariant reflectance features from visible and near-IR data.
  • Validated the appropriateness of the GFC for generating test datasets by comparing results with CIELab distance.
  • Showed that the modified algorithm can extract useful features from visible and near-UV data, despite spectral differences from blackbody models.

Conclusions:

  • The model-based color constancy algorithm can be effectively extended to the near-IR wavelength region for illuminant-invariant reflectance feature extraction.
  • The algorithm can be adapted to extract useful features from visible and near-UV data, broadening its applicability.
  • This research advances the capability to analyze surface properties across a wider spectral range.