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107
Color Constancy: A Method for Recovering Surface Spectral Reflectance
, 1986
"... this paper we describe an algorithm for estimating the surface reflectance functions of objects in a scene with incomplete knowledge of the spectral power distribution of the ambient light. We assume that lights and surfaces present in the environment are constrained in a way that we make explicit b ..."
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Cited by 191 (9 self)
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this paper we describe an algorithm for estimating the surface reflectance functions of objects in a scene with incomplete knowledge of the spectral power distribution of the ambient light. We assume that lights and surfaces present in the environment are constrained in a way that we make explicit below. ' An imageprocessing system using this algorithm can assign colors that are constant despite changes in the lighting on the scene. This capability is essential to correct color rendering in photography, in television, and in the construction of artificial visual systems for robotics. We describe how constraints on lights and surfaces in the environment make color constancy possible for a visual system and discuss the implications of the algorithm and these constraints for human color vision
Bayesian color constancy
 Journal of the Optical Society of America A
, 1997
"... The problem of color constancy may be solved if we can recover the physical properties of illuminants and surfaces from photosensor responses. We consider this problem within the framework of Bayesian decision theory. First, we model the relation among illuminants, surfaces, and photosensor response ..."
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Cited by 135 (18 self)
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The problem of color constancy may be solved if we can recover the physical properties of illuminants and surfaces from photosensor responses. We consider this problem within the framework of Bayesian decision theory. First, we model the relation among illuminants, surfaces, and photosensor responses. Second, we construct prior distributions that describe the probability that particular illuminants and surfaces exist in the world. Given a set of photosensor responses, we can then use Bayes’s rule to compute the posterior distribution for the illuminants and the surfaces in the scene. There are two widely used methods for obtaining a single best estimate from a posterior distribution. These are maximum a posteriori (MAP) and minimum meansquarederror (MMSE) estimation. We argue that neither is appropriate for perception problems. We describe a new estimator, which we call the maximum local mass (MLM) estimate, that integrates local probability density. The new method uses an optimality criterion that is appropriate for perception tasks: It finds the most probable approximately correct answer. For the case of low observation noise, we provide an efficient approximation. We develop the MLM estimator for the colorconstancy problem in which flat matte surfaces are uniformly illuminated. In simulations we show that the MLM method performs better than the MAP estimator and better than a number of standard colorconstancy algorithms. We note conditions under which even the optimal estimator produces poor estimates: when the spectral properties of the surfaces in the scene are biased. © 1997 Optical Society of America [S07403232(97)016074] 1.
Linear Models of Surface and Illuminant Spectra
 J. OPT. SOC. AM. A
, 1992
"... We describe procedures for creating efficient spectral representations for color. The representations generalize conventional tristimulus representations, which are based on the peripheral encoding by the human eye. We use lowdimensional linear models to approximate the spectral properties of surfa ..."
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Cited by 78 (1 self)
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We describe procedures for creating efficient spectral representations for color. The representations generalize conventional tristimulus representations, which are based on the peripheral encoding by the human eye. We use lowdimensional linear models to approximate the spectral properties of surfaces and illuminants with respect to a collection of sensing devices. We choose the linear model basis functions by minimizing the error in approximating sensor responses for collections of surfaces and illuminants. These linear models offer some conceptual simplifications for applications such as printer calibration; they also perform substantially better than principal components approximations for computer graphics applications.
Color Constancy: Generalized Diagonal Transforms Suffice
 J. Opt. Soc. Am. A
, 1994
"... This study's main result is to show that under the conditions imposed by the MaloneyWandell color constancy algorithm, whereby illuminants are three dimensional and reflectances two dimensional (the 32 world), color constancy can be expressed in terms of a simple independent adjustment of the sens ..."
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Cited by 71 (19 self)
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This study's main result is to show that under the conditions imposed by the MaloneyWandell color constancy algorithm, whereby illuminants are three dimensional and reflectances two dimensional (the 32 world), color constancy can be expressed in terms of a simple independent adjustment of the sensor responses (in other words, as a von Kries adaptation type of coefficient rule algorithm) as long as the sensor space is first transformed to a new basis. A consequence of this result is that any color constancy algorithm that makes 32 assumptions, such as the MaloneyWandell subspace algorithm, Forsyth's MWEXT, and the FuntDrew lightness algorithm, must effectively calculate a simple von Kriestype scaling of sensor responses, i.e., a diagonal matrix. Our results are strong in the sense that no constraint is placed on the initial spectral sensitivities of the sensors. In addition to purely theoretical arguments, we present results from simulations of von Kriestype color constancy in which the spectra of real illuminants and reflectances along with the humanconesensitivity functions are used. The simulations demonstrate that when the cone sensor space is transformed to its new basis in the appropriate manner a diagonal matrix supports nearly optimal color constancy. Key words: color constancy, von Kries, chromatic adaptation, color balancing. 1.
Asymmetric colormatching: How color appearance depends on the illuminant
 Journal of the Optical Society of America A
, 1992
"... Wereport the results of matching experiments designed to study the color appearance of objects rendered under different simulated illuminants on a CRT monitor. Subjects set asymmetric color matches between a standard object and a test object that were rendered under illuminants with different spectr ..."
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Cited by 45 (18 self)
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Wereport the results of matching experiments designed to study the color appearance of objects rendered under different simulated illuminants on a CRT monitor. Subjects set asymmetric color matches between a standard object and a test object that were rendered under illuminants with different spectral power distributions. For any illuminant change, we found that the mapping between the cone coordinates of matching standard and test objects was well approximated by a diagonal linear transformation. In this sense, our results are consistent
Diagonal transforms suffice for color constancy
 In ICCV93
, 1993
"... This paper’s main result is to show that under the conditions imposed by the Maloney Wandell color constancy algorithm, color constancy can in fact he expressed in terms of a simple independent adjustment of the sensor responsesin other words as a uon Kries adaptation type of coeficient rule alg ..."
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Cited by 35 (2 self)
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This paper’s main result is to show that under the conditions imposed by the Maloney Wandell color constancy algorithm, color constancy can in fact he expressed in terms of a simple independent adjustment of the sensor responsesin other words as a uon Kries adaptation type of coeficient rule algorithmso long as the sensor space is first transformed to a new hasis. Our overall goal is to present a theoretical analysis connecting many established theories of color constancy. For the case where surface refiectances are 2dimensional and illuminants are $dimensional, we prove that perfect color constancy can always be solved for by an independent adjustment of sensor responses, which means that the color constancy transform can he expressed as a diagonal matrix. This result requires a prior transformation of the sensor basis and to support it we show in particular that there exists n transformation of the original sensor basis under which the nondiagonal meth,ods of Maloney Wandell, Forsyth’s MWEXT and Funt and Drew’s lightness algorithm all reduce to simpler, diagonalmatrix theones of color constancy. Our results are strong in the sense that no constraint is placed on the initial sensor spectral sensitzuities. In addition to purely theoretical arguments, the paper contains results from simulations of diagonalmatrixbased color constancy in which the spectra of real illuminants and refiectances along with the human cone sensitivity functions are used. The simulations demonstrate that when the cone sensor space is transformed to its new basis in the appropriate manner, a diagonal matrix supports close to optimal color constancy. 1
Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions
 IEEE Trans. Geosci. Remote Sensing
, 1999
"... Abstract — The spectral radiance measured by an airborne imaging spectrometer for a material on the Earth’s surface depends strongly on the illumination incident of the material and the atmospheric conditions. This dependence has limited the success of materialidentification algorithms that rely on ..."
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Cited by 28 (1 self)
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Abstract — The spectral radiance measured by an airborne imaging spectrometer for a material on the Earth’s surface depends strongly on the illumination incident of the material and the atmospheric conditions. This dependence has limited the success of materialidentification algorithms that rely on hyperspectral image data without associated groundtruth information. In this paper, we use a comprehensive physical model to show that the set of observed 0.4–2.5 m spectralradiance vectors for a material lies in a lowdimensional subspace of the hyperspectralmeasurement space. The physical model captures the dependence of the reflected sunlight, reflected skylight, and pathradiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, we develop a local maximumlikelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. The algorithm requires only the spectral reflectance of the target material as input. We show that the low dimensionality of material subspaces allows for the robust discrimination of a large number of materials over a wide range of conditions. We demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions. Index Terms — Atmospheric correction, hyperspectral, invariant, material identification, HYDICE.
Learning as Extraction of LowDimensional Representations
 Mechanisms of Perceptual Learning
, 1996
"... Psychophysical findings accumulated over the past several decades indicate that perceptual tasks such as similarity judgment tend to be performed on a lowdimensional representation of the sensory data. Low dimensionality is especially important for learning, as the number of examples required for a ..."
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Cited by 26 (7 self)
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Psychophysical findings accumulated over the past several decades indicate that perceptual tasks such as similarity judgment tend to be performed on a lowdimensional representation of the sensory data. Low dimensionality is especially important for learning, as the number of examples required for attaining a given level of performance grows exponentially with the dimensionality of the underlying representation space. In this chapter, we argue that, whereas many perceptual problems are tractable precisely because their intrinsic dimensionality is low, the raw dimensionality of the sensory data is normally high, and must be reduced by a nontrivial computational process, which, in itself, may involve learning. Following a survey of computational techniques for dimensionality reduction, we show that it is possible to learn a lowdimensional representation that captures the intrinsic lowdimensional nature of certain classes of visual objects, thereby facilitating further learning of tasks...
Estimating the Scene Illumination Chromaticity by Using a Neural Network
 JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A
, 2002
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