Journal of the Optical Society of America A · 1986 · 242 citations · 15 references
MedicineVision ScienceColor VisionComputer VisionOpticsColor PerceptionReflectance ModelingColor NormalizationConstraintsLightnessNormalizationSpatial DecompositionSpectral Reflectance PropertiesSurface Reflectance Estimation
The computational problem underlying color vision is to recover the invariant surface‑spectral‑reflectance properties of an object. The study aims to clarify and formalize the lightness problem by proposing a new intensity‑equation formulation and outlining spatial decomposition and spectral normalization subproblems. The authors review existing lightness algorithms and introduce a multiple‑scales extension, based on a newly formulated intensity equation. They show that all lightness algorithms can be derived from a single mathematical formula, revealing shared limitations imposed by physical constraints and boundary conditions.
The computational problem underlying color vision is to recover the invariant surface-spectral-reflectance properties of an object. Lightness algorithms, which recover an approximation to surface reflectance in independent wavelength channels, have been proposed as one method to compute color. This paper clarifies and formalizes the lightness problem by proposing a new formulation of the intensity equation on which lightness algorithms are based and by identifying and discussing two basic subproblems of lightness and color computation: spatial decomposition and spectral normalization of the intensity signal. Several lightness algorithms are reviewed, and a new extension (the multiple-scales algorithm) of one of them is proposed. The main computational result is that each of the lightness algorithms may be derived from a single mathematical formula, under different conditions, which, in turn, imply limitations for the implementation of lightness algorithms by man or machine. In particular, the algorithms share certain limitations on their implementation that follow from the physical constraints imposed on the statement of the problem and the boundary conditions applied in its solution.
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Edwin H. Land, John J. McCann · Journal of the Optical Society of America · 1971 · 4K citations
Medicine, Physiology, Vision Science +14
A Reflectance Model for Computer Graphics
Robert L. Cook, K. E. Torrance · ACM Transactions on Graphics · 1982 · 1.4K citations · Full text
Understanding image intensities
Berthold K. P. Horn · Artificial Intelligence · 1977 · 696 citations