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

Computing Chromatic Adaptation

Most of today’s chromatic adaptation transforms (CATs) are based on a modified form of the von Kries chromatic adaptation model, which states that chromatic adaptation is an independent gain regulation of the three photoreceptors in the human visual system. However, modern CATs apply the scaling not in cone space, but use “sharper” sensors, i.e. sensors that have a narrower shape than cones. The recommended transforms currently in use are derived by minimizing perceptual error over experimentally obtained corresponding color data sets. We show that these sensors are still not optimally sharp. Using different computational approaches, we obtain sensors that are even more narrowband. In a first experiment, we derive a CAT by using spectral sharpening on Lam’s corresponding color data set. The resulting Sharp CAT, which minimizes XYZ errors, performs as well as the current most popular CATs when tested on several corresponding color data sets and evaluating perceptual error. Designing a spherical sampling technique, we can indeed show that these CAT sensors are not unique, and that there exist a large number of sensors that perform just as well as CAT02, the chromatic adaptation transform used in CIECAM02 and the ICC color management framework. We speculate that in order to make a final decision on a single CAT, we should consider secondary factors, such as their applicability in a color imaging workflow. We show that sharp sensors are very appropriate for color encodings, as they provide excellent gamut coverage and hue constancy. Finally, we derive sensors for a CAT that provide stable color ratios over different illuminants, i.e. that only model physical responses, which still can predict experimentally obtained appearance data. The resulting sensors are sharp

Apport du contenu visuel à l'adaptation chromatique

Les systèmes de capture d'images tels que les scanners, les caméras et les appareils photos numériques, n'ont pas l'habilité à s'adapter dynamiquement au changement d'illumination comme le système visuel humains. Ainsi, pour reproduire fidèlement l'apparence d'une image couleur, les systèmes de formation et de traitement d'images ont besoin d'appliquer une transformation qui convertit les couleurs capturées sous un illuminant d'entrée, vers des couleurs correspondantes sous un illuminant de sortie. Cette transformation est appelée, transformation pour l'adaptation chromatique, connue dans les étapes de formation physique d'image par la balance du blanc. L'adaptation chromatique est une transformation linéaire simple à implémenter. C'est un avantage qui la rend adaptée aux dispositifs à faible énergie, tel que les PDAs et les appareils photos numériques intégrés dans les téléphones portables. Dans ce mémoire, nous abordons l'adaptation chromatique d'un point de vue incluant le contenu visuel de la scène. Dans cette perspective, nous commençons par examiner l'influence de l'adaptation chromatique sur le contenu de l'image. Par la suite, nous proposons une reformulation mathématique de la transformation Sharp en se basant sur le contenu de l'image, et en incluant des contraintes liées à la structure du capteur, tel que le chevauchement entre réponses spectrales des différentes bandes, et la préservation du gamut du capteur

A Perception-based Color Space for Illumination-invariant Image Processing

Motivated by perceptual principles, we derive a new color space in which the associated metric approximates perceived distances and color displacements capture relationships that are robust to spectral changes in illumination. The resulting color space can be used with existing image processing algorithms with little or no change to the methods.Engineering and Applied Science

Relational color constancy in achromatic and isoluminant images

Relational color constancy, which refers to the constancy of perceived relations between surface colors under changes in illuminant, may be based on the computation of spatial ratios of cone excitations. As this activity need occur only within rather than between cone pathways, relational color constancy might be assumed to be based on relative luminance processing. This hypothesis was tested in a psychophysical experiment in which observers viewed simulated images of Mondrian patterns undergoing colorimetric changes that could be attributed either to an illuminant change or to a nonilluminant change; the images were isoluminant, achromatic, or unmodified. Observers reliably discriminated the two types of changes in all three conditions, implying that relational color constancy is not based on luminance cues alone. A computer simulation showed that in these isoluminant and achromatic images spatial ratios of cone excitations and of combinations of cone excitations were almost invariant under illuminant changes and that discrimination performance could be predicted from deviations in these ratios.Biotechnology and Biological Sciences Research Council (BBSRC