Color Constancy from Mutual Reflection

Mutual reflection occurs when light reflected from one surface illuminates a second surface. In this situation, the color of one or both surfaces can be modified by a color-bleeding effect. In this article we examine how sensor values (e.g., RGB values) are modified in the mutual reflection region and show that a good approximation of the surface spectral reflectance function for each surface can be recovered by using the extra information from mutual reflection. Thus color constancy results from an examination of mutual reflection. Use is made of finite dimensional linear models for ambient illumination and for surface spectral reflectance. If m and n are the number of basis functions required to model illumination and surface spectral reflectance respectively, then we find that the number of different sensor classes p must satisfy the condition p≥(2 n+m)/3. If we use three basis functions to model illumination and three basis functions to model surface spectral reflectance, then only three classes of sensors are required to carry out the algorithm. Results are presented showing a small increase in error over the error inherent in the underlying finite dimension models

Low levels of specularity support operational color constancy, particularly when surface and illumination geometry can be inferred

We tested whether surface specularity alone supports operational color constancy—the ability to discriminate changes in illumination or reflectance. Observers viewed short animations of illuminant or reflectance changes in rendered scenes containing a single spherical surface and were asked to classify the change. Performance improved with increasing specularity, as predicted from regularities in chromatic statistics. Peak performance was impaired by spatial rearrangements of image pixels that disrupted the perception of illuminated surfaces but was maintained with increased surface complexity. The characteristic chromatic transformations that are available with nonzero specularity are useful for operational color constancy, particularly if accompanied by appropriate perceptual organization

Color Relationism and Enactive Ontology

In this paper, I present the enactive theory of color that implies a form of color relationism. I argue that this view constitutes a better alternative to color subjectivism and color objectivism. I liken the enactive view to Husserl’s phenomenology of perception, arguing that both deconstruct the clear duality of subject and object, which is at the basis of the other theories of color, in order to claim the co-constitution of subject and object in the process of experience. I also extend the enactive and phenomenological account of color to the more general topic of the epistemological and ontological status of sensory qualities (qualia), outlining the fields of enactive phenomenology and enactive ontology

Modeling the Evolution of a cluster of gravitating bodies taking into account their absolutely inelastic collisions

Numerical simulation of evolution of a cluster of a finite number of gravitating bodies interacting only by their intrinsic gravity has been carried out. The goal of the study was to reveal the main characteristic phases of the spatial distribution of material bodies constituting the cluster. In solving the problem, the possibility of interbody collisions was taken into account, the collisions being assumed to be absolutely inelastic. Forces external to the body cluster under consideration were ignored. Among all the internal force factors acting within the cluster, only the gravitational interaction was taken into account. The total mass of all the gravitating bodies of the cluster was assumed to remain constant during the entire evolution. The Cauchy problem with natural initial conditions was considered. To check the process of solution, the so-called rotation curve was used which represents the current radial distribution of orbital velocities of the cluster bodies. The numerical analysis showed time variations of the model cluster rotation curve and, particularly, the fact that the rotation curve horizontal section is only a short moment in evolution of the gravitating bodies cluster. The results obtained within the scope of classical mechanics show that it is possible to represent all the rotation curve variations for the observed galaxies without appealing to the hypothesis of non-observable gravitating "dark matter".Comment: 12 pages, 10 figures, 1 tabl

Ridge Regression Approach to Color Constancy

This thesis presents the work on color constancy and its application in the field of computer vision. Color constancy is a phenomena of representing (visualizing) the reflectance properties of the scene independent of the illumination spectrum. The motivation behind this work is two folds:The primary motivation is to seek ‘consistency and stability’ in color reproduction and algorithm performance respectively because color is used as one of the important features in many computer vision applications; therefore consistency of the color features is essential for high application success. Second motivation is to reduce ‘computational complexity’ without sacrificing the primary motivation.This work presents machine learning approach to color constancy. An empirical model is developed from the training data. Neural network and support vector machine are two prominent nonlinear learning theories. The work on support vector machine based color constancy shows its superior performance over neural networks based color constancy in terms of stability. But support vector machine is time consuming method. Alternative approach to support vectormachine, is a simple, fast and analytically solvable linear modeling technique known as ‘Ridge regression’. It learns the dependency between the surface reflectance and illumination from a presented training sample of data. Ridge regression provides answer to the two fold motivation behind this work, i.e., stable and computationally simple approach. The proposed algorithms, ‘Support vector machine’ and ‘Ridge regression’ involves three step processes: First, an input matrix constructed from the preprocessed training data set is trained toobtain a trained model. Second, test images are presented to the trained model to obtain the chromaticity estimate of the illuminants present in the testing images. Finally, linear diagonal transformation is performed to obtain the color corrected image. The results show the effectiveness of the proposed algorithms on both calibrated and uncalibrated data set in comparison to the methods discussed in literature review. Finally, thesis concludes with a complete discussion and summary on comparison between the proposed approaches and other algorithms

Estimation of illuminants from color signals of illuminated objects

Color constancy is the ability of the human visual systems to discount the effect of the illumination and to assign approximate constant color descriptions to objects. This ability has long been studied and widely applied to many areas such as color reproduction and machine vision, especially with the development of digital color processing. This thesis work makes some improvements in illuminant estimation and computational color constancy based on the study and testing of existing algorithms. During recent years, it has been noticed that illuminant estimation based on gamut comparison is efficient and simple to implement. Although numerous investigations have been done in this field, there are still some deficiencies. A large part of this thesis has been work in the area of illuminant estimation through gamut comparison. Noting the importance of color lightness in gamut comparison, and also in order to simplify three-dimensional gamut calculation, a new illuminant estimation method is proposed through gamut comparison at separated lightness levels. Maximum color separation is a color constancy method which is based on the assumption that colors in a scene will obtain the largest gamut area under white illumination. The method was further derived and improved in this thesis to make it applicable and efficient. In addition, some intrinsic questions in gamut comparison methods, for example the relationship between the color space and the application of gamut or probability distribution, were investigated. Color constancy methods through spectral recovery have the limitation that there is no effective way to confine the range of object spectral reflectance. In this thesis, a new constraint on spectral reflectance based on the relative ratios of the parameters from principal component analysis (PCA) decomposition is proposed. The proposed constraint was applied to illuminant detection methods as a metric on the recovered spectral reflectance. Because of the importance of the sensor sensitivities and their wide variation, the influence from the sensor sensitivities on different kinds of illuminant estimation methods was also studied. Estimation method stability to wrong sensor information was tested, suggesting the possible solution to illuminant estimation on images with unknown sources. In addition, with the development of multi-channel imaging, some research on illuminant estimation for multi-channel images both on the correlated color temperature (CCT) estimation and the illuminant spectral recovery was performed in this thesis. All the improvement and new proposed methods in this thesis are tested and compared with those existing methods with best performance, both on synthetic data and real images. The comparison verified the high efficiency and implementation simplicity of the proposed methods

Can illumination estimates provide the basis for color constancy?

Objects hardly appear to change color when the spectral distribution of the illumination changes: a phenomenon known as color constancy. Color constancy could either be achieved by relying on properties that are insensitive to changes in the illumination (such as spatial color contrast) or by compensating for the estimated chromaticity of the illuminant. We examined whether subjects can judge the illuminant's color well enough to account for their own color constancy. We found that subjects were very poor at judging the color of a lamp from the light reflected by the scene it illuminated. They were much better at judging the color of a surface within the scene. We conclude that color constancy must be achieved by relying on relationships that are insensitive to the illumination rather than by explicitly judging the color of the illumination. © ARVO

Spatial distributions of local illumination color in natural scenes

In natural complex environments, the elevation of the sun and the presence of occluding objects and mutual reflections cause variations in the spectral composition of the local illumination across time and location. Unlike the changes in time and their consequences for color appearance and constancy, the spatial variations of local illumination color in natural scenes have received relatively little attention. The aim of the present work was to characterize these spatial variations by spectral imaging. Hyperspectral radiance images were obtained from 30 rural and urban scenes in which neutral probe spheres were embedded. The spectra of the local illumination at 17 sample points on each sphere in each scene were extracted and a total of 1904 chromaticity coordinates and correlated color temperatures (CCTs) derived. Maximum differences in chromaticities over spheres and over scenes were similar. When data were pooled over scenes, CCTs ranged from 3000 K to 20,000 K, a variation of the same order of magnitude as that occurring over the day. Any mechanisms that underlie stable surface color perception in natural scenes need to accommodate these large spatial variations in local illumination color.This work was supported by the Centro de Física of Minho University, Braga, Portugal, by the European Regional Development Fund through Program COMPETE (FCOMP-01-0124-FEDER-009858/029564), by the National Portuguese funds through Fundação para a Ciência e a Tecnologia, Portugal (Grants PTDC/EEA-EEL/098572/2008 and PTDC/MHC-PCN/4731/2012), and by the Engineering and Physical Sciences Research Council, United Kingdom (Grants GR/R39412/01, EP/B000257/1 and EP/E056512/1). We thank Paulo D. A. Pinto and João M. M. Linhares for collaboration in the acquisition of hyperspectral data of some scenes and Paulo D. A. Pinto for the preparation of the gray spheres

Extending minkowski norm illuminant estimation

The ability to obtain colour images invariant to changes of illumination is called colour constancy. An algorithm for colour constancy takes sensor responses - digital images - as input, estimates the ambient light and returns a corrected image in which the illuminant influence over the colours has been removed. In this thesis we investigate the step of illuminant estimation for colour constancy and aim to extend the state of the art in this field. We first revisit the Minkowski Family Norm framework for illuminant estimation. Because, of all the simple statistical approaches, it is the most general formulation and, crucially, delivers the best results. This thesis makes four technical contributions. First, we reformulate the Minkowski approach to provide better estimation when a constraint on illumination is employed. Second, we show how the method can (by orders of magnitude) be implemented to run much faster than previous algorithms. Third, we show how a simple edge based variant delivers improved estimation compared with the state of the art across many datasets. In contradistinction to the prior state of the art our definition of edges is fixed (a simple combination of first and second derivatives) i.e. we do not tune our algorithm to particular image datasets. This performance is further improved by incorporating a gamut constraint on surface colour -our 4th contribution. The thesis finishes by considering our approach in the context of a recent OSA competition run to benchmark computational algorithms operating on physiologically relevant cone based input data. Here we find that Constrained Minkowski Norms operi ii ating on spectrally sharpened cone sensors (linear combinations of the cones that behave more like camera sensors) supports competition leading illuminant estimation