Recent algorithms developed in the field of color vision make assumptions based on the spectral reflectance curves of Munsell chips and natural materials. Some of them rely on data collected many years ago. which is partially incomplete in the visible spectrum. or contains many occurrences of the same material in it. In this article. we present a set of new measurements of different materials. In particular. we measured the spectral reflectance of Munsell chips, paints. and various natural materials in the 390–730‐nm range. In addition, we have analyzed. through principal‐component analysis, the possibility of representing the data collected with a set of basis functions. We show the implications of varying the number of principal components used (from 7 down to 3) on the errors introduced using this method.

The task of distinguishing material changes from shadow boundaries in chromatic images is discussed. Although there have been previous attempts at providing solutions to this problem, the assumptions that were adopted were too restrictive. Using a simple reflection model, we show that the ambient illumination cannot be assumed to have the same spectral characteristics as the incident illumination, since it may lead to the classification of shadow boundaries as material changes. In such cases, we show that it is necessary to take into account the spectral properties of the ambient illumination in order to develop a technique that is more robust and stable than previous techniques. This technique uses a biologically motivated model of color vision and, in particular, a set of chromatic-opponent and double-opponent center-surround operators. We apply this technique to simulated test patterns as well as to a chromatic image. It is shown that, given some knowledge about the strength of the ambient illumination, this method provides a better classification of shadow boundaries and material changes.

We propose a method, based on finite-dimensional linear models of rejection and illumination, which allows the transformation of chromatic images into color constant images. This proves to be useful in applications in which either there is no information on the illuminant present in the scene, or when such information is confounded by the existence of inter-rejections between objects. The methodproposed in this paper is aimed at computations taking place beyond the sensory level of vision systems, and may use inputs corrected by sensors Cfor adaptation). In contrast to previous work, we show that good results can be obtained using a 3-receptor system and some knowledge about the spectral properties of natural materials and illuminants. In the method developed, an estimate of the illuminant in the scene is computed, which allow the computation of color constant descriptors of the pixel values in the image. In addition, we show a method of computing an estimate to the actual rejectances of the materials in the scene out of the computed color constant descriptors. descriptors. z. If the original [R,G,B] values of the image are defined as PR, PG and Ps,* then the following relation can be formulated: A * -d = (a).The main goal of the workdescribed in this article is to find the values of the vector, C , which for each pixel, is a set of (three) numbers representing the color of the material, regardless of the illumination. Note, however, that by virtue of the technique discussed in the following sections, an approximation of the actual reflecLance of the material can also be extracted from the vector C . This will be discussed in the Discussion. Previous WorkSome of the early work on the problem of color constancy began with that of von Kries.2 He postulated that, although the responses of the three cone mechanisms are affected *Note that PR, PG, and PS are sets of pixels and as such are actually a function of spatial location.

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