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Understanding Color Perception in Human Vision Systems: Linear Models for Data Representation

This article delves into how linear models help in understanding color perception contexts and the significance of compact data representation in human vision systems. It explores the impact of illumination on color appearance and the role of surface reflectance functions. Examples like Daylights and Macbeth Chart Basis Functions are used to illustrate these concepts.

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Understanding Color Perception in Human Vision Systems: Linear Models for Data Representation

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  1. Human Perception 9: Color

  2. Color Appearance and Context

  3. Color Matching and Context

  4. Color Signaling Pathway

  5. Surface Reflectance Functions

  6. Illumination Effect on Absorptions

  7. Why are Linear Models Useful? • Compact representation of the data • Work smoothly with conventional linear matrix computations • Help understand practical matters like the number of sensors needed to measure a set of signals

  8. Example Daylights

  9. Daylight Basis Functions (CIE)

  10. Macbeth Chart: Reflectance

  11. Macbeth Chart Basis Functions

  12. Macbeth Approximations: Functions

  13. Macbeth Approximations: Appearance

  14. Illumination(3) and Reflectance(3) Yellow illuminant Blue illuminant Black surface White surface

  15. Surface & Illuminant Estimation • Gray world assumption Average of all the surfaces in the image is gray • Uniform perfect reflector The brightest surface in the image • Specularities Glossy surface reflects the illumination directly

  16. Color Signaling Pathway

  17. Example Daylights

  18. Macbeth Chart Basis Functions

  19. The Photoreceptor Types • Cone wavelength absorptions

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