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Computational luminance constancy from naturalistic images.

Vijay Singh1, Nicolas P Cottaris2, Benjamin S Heasly2

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This study reveals how the human visual system estimates object color despite changing light. Understanding luminous reflectance factor (LRF) estimation helps explain color constancy.

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Area of Science:

  • Visual perception
  • Computational neuroscience
  • Color science

Background:

  • Human color perception remains stable despite varying illumination.
  • Luminous reflectance factor (LRF) quantifies object color under standard light.
  • Understanding LRF estimation is key to explaining color constancy.

Purpose of the Study:

  • Investigate how naturalistic scene variations affect target object LRF estimation.
  • Analyze the impact of object reflectance, illumination spectra, and background on LRF accuracy.
  • Develop a framework for assessing scene variability's limits on luminance constancy.

Main Methods:

  • Applied supervised statistical learning to simulated cone photoreceptor excitations.
  • Utilized computer graphics to render naturalistic images with statistical models of spectral variation.
  • Employed linear receptive fields operating on cone excitation contrast for LRF decoding.

Main Results:

  • Estimates of target object LRF were within 13% of the correct value.
  • Demonstrated the impact of target reflectance, illumination spectra, and background on LRF estimation.
  • Quantified performance limits on luminance constancy due to scene variability.

Conclusions:

  • Developed a computational framework for studying color constancy.
  • Identified key scene properties influencing luminous reflectance factor estimation.
  • Provided insights into the visual system's mechanisms for stable color perception under varying light conditions.