In the standard reflectance model for inhomogeneous materials it is assumed that light is reflected by two independent mechanisms. One component is reflected at the interface of the material and air. Light reflected by this mechanism does not interact with surface colorant, and its spectral composition is assumed to equal that of the incident light. The second component is reflected after entering and interacting with the subsurface structure of the material. This interaction substantially changes the spectral composition of the reflected light. We adopt a vector analysis technique for testing the standard reflectance model. Further, we develop a computational method to determine the components of the observed spectra, and we obtain an estimate of the illuminant without using a reference white standard. Finally, we evaluate the accuracy of the standard model and the feasibility of the illuminant spectral estimation by using several test objectives.

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Knowledge of the scene illuminant spectral power distribution is useful for many imaging applications, such as color image reproduction and automatic algorithms for image database applications. In many applications accurate spectral characterization of the illuminant is impossible because the input device acquires only three spectral samples. In such applications it is sensible to set a more limited objective of classifying the illuminant as belonging to one of several likely types. We describe a data set of natural images with measured illuminants for testing illuminant classification algorithms. One simple type of algorithm is described and evaluated by using the new data set. The empirical measurements show that illuminant information is more reliable in bright regions than in dark regions. Theoretical predictions of the algorithm's classification performance with respect to scene illuminant blackbody color temperature are tested and confirmed by using the natural-image data set.

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This paper describes practical algorithms and experimental results concerning illuminant classification. Specifically, we review the sensor correlation algorithm for illuminant classification and we discuss four changes that improve the algorithm's estimation accuracy and broaden its applicability. First, we space the classification illuminants evenly along the reciprocal scale of color temperature, called "mired," rather than the original color-temperature scale. This improves the perceptual uniformity of the illuminant classification set. Second, we calculate correlation values between the image color gamut and the reference illuminant gamut, rather than between the image pixels and the illuminant gamuts. This change makes the algorithm more reliable. Third, we introduce a new image scaling operation to adjust for overall intensity differences between images. Fourth, we develop the three-dimensional classification algorithms using all three-color channels and compare this with the original two algorithms from the viewpoint of accuracy and computational efficiency. The image processing algorithms incorporating these changes are evaluated using a real image database with calibrated scene illuminants.

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This article proposes dichromatic reflection models adequate for describing surface‐spectral reflectances of a variety of materials. the standard dichromatic reflection model for inhomogeneous materials assumes that light reflected from an object's surface is decomposed into two additive components: the body reflection and the interface reflection, which is independent of wavelength. First, we present a method for testing the adequacy of this standard model based on the observations of surface‐spectral reflectances. the standard dichromatic reflection model is adequate for many materials. However, the test results point out that there are some cloths for which the standard model is inadequate, and metals that have only the interface reflection. Next, we extend the standard model to have an interface reflection component that depends on wavelength. an algorithm is presented for estimating two reflectance functions in the generalized model. Moreover, a dichromatic reflection model specialized for only the specular reflection is defined for approximating metal's reflection. the reflection characteristics are analyzed using a chromaticity diagram. It is concluded that surfacespectral reflectances of most materials are described by three types of the dichromatic reflection model. © 1994 John Wiley & Sons, Inc.