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 image-processing 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

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We describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin’s theory of natural selection. Simulations of Bayesian natural selection were found to yield new insights, for example, into the co-evolution of camouflage, color vision, and decision criteria. The Bayesian framework captures and generalizes, in a formal way, many of the important ideas of other approaches to perception and cognition.

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...

In this report we propose a new model to represent spectral functions called the composite model. This model is

Given only a color camera's RGB measurement of a complete color signal spectrum, how can the spectrum be estimated? We propose and test a new method that answers this question and recovers an approximating spectrum. Although this approximation has intrinsic interest, our main focus is on using it to generate tristimulus values for color reproduction. In essence, this provides a new method of converting color camera signals to tristimulus coordinates, because a spectrum de nes a unique point in tristimulus coordinates. Color reproduction is founded on producing spectra that are metamers to those appearing in the original scene. Once a spectrum's tristimulus coordinates are known, generating a metamer is a well de ned problem. Unfortunately, most color cameras cannot produce the necessary tristimulus coordinates directly because their color separation lters are not related by a linear transformation to the human color-matching functions. Color cameras are more likely to reproduce colors that look correct to the camera than to a human observer. Conversion from camera RGB triples to tristimulus values will always involve some type of estimation procedure unless cameras are redesigned. We compare the accuracy of our conversion strategy to that of one based on Horn's work on the exact reproduction of colored images. Our new method relies on expressing the color signal spectrum in terms of a linear combination of basis functions. The results show that a principal component analysis in color-signal space yields the best basis for our purposes, since using it leads to the most \natural " color signal spectrum that is statistically likely to have generated a given camera signal. 2 Natural Metamers 3

Identifying or matching the surface color of a moving object in surveillance video is critical for achieving reliable object-tracking and searching. Traditional color models provide little help, since the surface of an object is usually not flat, the object’s motion can alter the surface’s orientation, and the lighting conditions can vary when the object moves. To tackle this research problem, we conduct extensive data mining on video clips collected under various lighting conditions and distances from several video-cameras. We observe how each of the eleven culture colors can drift in the color space when an object’s surface is in motion. In the color space, we then learn the drift pattern of each culture color for classifying unseen surface colors. Finally, we devise a distance function taking color drift into consideration to perform color identification and matching. Empirical studies show our approach to be very promising: achieving over 95 % color-prediction accuracy. 1

The image recorded by a camera depends on three factors: The physical content of the scene, the illumination incident on the scene, and the characteristics of the camera. This leads to a problem for many applications where the main interest is in the physical content of the scene. Consider, for example, a computer

This paper analyzes the variation of the color of objects with respect to existing models of daylight and surface reflectance, and develops context-based models of daylight (based on the CIE model [18]) and hybrid surface reflectance (based on existing hybrid surface reflectance models [21, 25, 32, 35]) called the Normalized Photometric Function. Thereafter, given the time-of-day (which, along with location, is used to calculate the sun-angle [23]), approximate cloud cover and sun-visibility, the color of the incident daylight is predicted, and combined with the reflectance model of the target object to predict the apparent color of the object; image pixels are then classified based on the predicted color. Section 2 describes the causes for the variation in apparent color; section 3 gives a brief literature review; section 4 describes the CIE daylight model and the context-based daylight model developed in this study; section 5 analyzes surface reflectance with respect to existing models and then develops the Normalized Photometric Function (NPF) model; section 6 combines the daylight and NPF models for context-based color prediction; finally, section 7 summarizes the conclusions of the study. 2 Causes for color shift in outdoor scenes

We describe spectral estimation principles that are useful for color balancing, color conversion, and sensor design. The principles extend conventional estimation methods, which rely on linear models of the input data, by characterizing the distribution or structure of the linear model coefficients. When the linear model coefficients of the input data are highly structured, it is possible to improve the quality of a simple linear model by estimating coefficients that are invisible to the sensors. We illustrate these principles using the synthetic example of estimating blackbody radiator spectral power distributions. Then, we apply the principles to typical daylight illuminants that we measured over the course of twenty days in Stanford, California. We show that the distribution of the daylight linear model coefficients that approximate the daylight spectral power distributions are highly structured. We further show that from knowledge of the coefficient structure, nonlinear algorithms ...