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 low-dimensional 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.

The paper proposes a number of models to examine through what mech-anisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although the paper takes no stance on which position is to be accepted as final truth with respect to hu-man categorisation and naming, it points to theoretical constraints that make each position more or less likely and contains clear suggestions on what the best engineering solution would be. Specifically, it argues that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication.

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Abstract—In inhomogeneous objects, highlights are linear combinations of diffuse and specular reflection components. A number of methods have been proposed to separate or decompose these two components. To our knowledge, all methods that use a single input image require explicit color segmentation to deal with multicolored surfaces. Unfortunately, for complex textured images, current color segmentation algorithms are still problematic to segment correctly. Consequently, a method without explicit color segmentation becomes indispensable and this paper presents such a method. The method is based solely on colors, particularly chromaticity, without requiring any geometrical information. One of the basic ideas is to iteratively compare the intensity logarithmic differentiation of an input image and its specular-free image. A specular-free image is an image that has exactly the same geometrical profile as the diffuse component of the input image and that can be generated by shifting each pixel’s intensity and maximum chromaticity nonlinearly. Unlike existing methods using a single image, all processes in the proposed method are done locally, involving a maximum of only two neighboring pixels. This local operation is useful for handling textured objects with complex multicolored scenes. Evaluations by comparison with the results of polarizing filters demonstrate the effectiveness of the proposed method. Index Terms—Reflection components separation, specular reflection, diffuse reflection, dichromatic reflection model, chromaticity, specular-to-diffuse mechanism, specular-free image. 1

We introduce an active imaging method to measure scene illumination. The system implementation is divided into four steps. First, the system acquires two images: one is an ordinary image of the scene under ambient light and the other is a corresponding image in which light from the camera flash is added to the scene. Second, the image pair is analyzed to obtain an image that represents the scene as if it had been illuminated by the flash alone. Third, the flashonly image is used to estimate object reflectance functions. Fourth, using the estimated reflectance functions, the ambient illumination spectral power distribution is estimated. We present results that evaluate the method’s stability with respect to changes in the mean reflectance function of the scene. Finally, we discuss limitations of the current implementation and alternative implementations.

Many vision tasks such as scene segmentation, or the recognition of materials within a scene, become considerably easier when it is possible to measure the spectral reflectance of scene surfaces. In this paper, we present an efficient and robust approach for recovering spectral reflectance in a scene that combines the advantages of using multiple spectral sources and a multispectral camera. We have implemented a system based on this approach using a cluster of light sources with different spectra to illuminate the scene and a conventional RGB camera to acquire images. Rather than sequentially activating the sources, we have developed a novel technique to determine the optimal multiplexing sequence of spectral sources so as to minimize the number of acquired images. We use our recovered spectral measurements to recover the continuous spectral reflectance for each scene point by using a linear model for spectral reflectance. Our imaging system can produce multispectral videos of scenes at 30fps. We demonstrate the effectiveness of our system through extensive evaluation. As a demonstration, we present the results of applying data recovered by our system to material segmentation and spectral relighting. 1.

INTRODUCTION Reflectance spectra are physical properties of materials, which can be utilized as cues to identify different objects. However, the light reaching a visual system is the combination of reflectance and illumination spectra (defined as color signals in the present study). For a long time, color vision has been recognized as having the ability to recover reflectance under various illuminants in order to achieve color constancy. 1--4 Most computational models of color constancy involve the assumption that reflectance spectra of objects can be described by a few basis functions (for a review see Ref. 5). In turn, visual systems with two or more different photoreceptor types are able to estimate the weights of these basis functions and thereby to recover reflectance spectra of objects. 3--6 Therefore the characterization of reflectance spectra is an important step to understanding color vision. Historically, the properties of reflectance spectra of natural objects were fir

Outdoor scene classification is challenging due to irregular geometry, uncontrolled illumination, and noisy reflectance distributions. This paper discusses a Bayesian approach to classifying a color image of an outdoor scene. A likelihood model factors in the physics of the image formation process, the sensor noise distribution, and prior distributions over geometry, material types, and illuminant spectrum parameters. These prior distributions are learned through a training process that uses color observations of planar scene patches over time. An iterative linear algorithm estimates the maximum likelihood reflectance, spectrum, geometry, and object class labels for a new image. Experiments on images taken by outdoor surveillance cameras classify known material types and shadow regions correctly, and flag as outliers material types that were not seen previously. 1.

Introduction In this chapter we describe a system for the acquisition of multispectral images using a CCD camera with carefully selected optical filters. We further present an application where the acquired multispectral images are used to simulate the image of a scene as it would have appeared under a given illuminant. For this application, the use of multispectral images is found to yield a much higher accuracy compared to traditional methods using only three-channel colour images. A multispectral image is an image where each pixel contains information about the spectral reflectance of the imaged scene. Multispectral images carry information about a number of spectral bands: from three components per pixel for colour images to several hundreds of bands for hyperspectral images. Multispectral imaging is relevant to several domains of application, such as remote sensing [1], astronomy [2], physics, analysis of museological objects [3, 4], cosmetics, medicine [5], high-accuracy

The light recorded by a camera is a function of the scene illumination, the reflective characteristics of the objects in the scene, and the camera sensors. The goal of color constancy is to separate the effect of the illumination from that of the reflectances. In this work, this takes the form of mapping images taken under an unknown light into images which are estimates of how the scene would appear under a fixed, known light. The research into color constancy has yielded a number of disparate theoretical results, but testing on image data is rare. The thrust of this work is to move towards a comprehensive algorithm which is applicable to image data. Necessary preparatory steps include measuring the illumination and reflectances expected in real scenes, and determining the camera response function. Next, a number of color constancy algorithms are implemented, with emphasis on the gamut mapping approach introduced by D. Forsyth and recently extended by G. Finlayson. These algorithms al...