Color constancy and metamerism are two phenomena that take place when the spectral power distribution of illuminant is changed. The difference between the Color inconstancies of the metameric pairs is the source of metamerism.In present study, 98 actual metameric pairs were used to find the relation between these phenomena. The results showed that after parameric correction, the value of metameric index in the Color difference group was equal to difference between their Color inconstancy indices with acceptable approximation. The accuracy increased when the metameric indices were calculated after adaptation.

Face recognition is a challenging problem due to different illuminations, poses, facial expressions, and occlusions. In this paper, a new robust face recognition method is proposed based on the Color and edge orientation difference histogram. Firstly, the Color and edge orientation difference histogram is extracted using Color, Color difference, edge orientation, and edge orientation difference of the face image. Then the backward feature selection is employed in order to reduce the number of features. Finally, the Canberra measure is used to assess the similarity between the images. The Color and edge orientation difference histogram shows the Color and edge orientation difference between two neighboring pixels. This histogram is effective for face recognition due to the different skin Colors and different edge orientations of the face image, which leads to a different light reflection. The proposed method is evaluated on the Yale and ORL face datasets. These datasets consist of gray-scale face images under different illuminations, poses, facial expressions, and occlusions. The recognition rate over the Yale and ORL datasets is achieved to be 100% and 98. 75%, respectively. The experimental results demonstrate that the proposed method outperforms the existing methods in face recognition.

A double ambiguity has been charged against Rawls’s difference principle (DP). Is it Maximin, Leximin, or something else? Usually, following A. Sen, scholars identify DP with the so-called Leximin. One argues here that one has to distinguish 1° the Leximin, 2° the Maximin (as rule of justice formally analogous to the maximin rule of decision), represented by the figure in L of the perfectly substitutable goods, and 3° the genuine DP. When the augmentation of inequality benefits the worse off, only Pareto-strong improvements are permitted. Leximin would also permit Pareto-weak improvements too (after the first maximum D), where only the richest improves: from (2, 3) to (2, 5), say. This is forbidden by DP. With two classes, unlike Maximin, DP has no curve of indifference and is always decisive, as Leximin is. For undecisive Rules of Justice, which admit indifferent curves, I propose to add a lexically secondary rule, to break ties. That move is able to clarify the links and the differences between on the one hand Maximin alone, with its typical indifference curves in L, and on the other hand, the DP properly understood and the Leximin, which both have no indifferent curves. With two classes of persons (best off/worse off), DP appears more egalitarian than Leximin, because it's secondary rule is MinIn (Minimization of Inequality). But the intuition behind the distinction is that it cannot possible “fair” that only the best off improves in a productive social cooperation.

Background and Aims: Increase of esthetic demands among dental patients has led to development and wide spread use of tooth Colored ceramic systems for fabricating fixed restorations. However, they do not have same capability of matching the target Color. The aim of this study was to compare the Color coordinates of three widely used systems with A2 tab from a Vita Lumin Classic shade guide.Materials and Methods: In this study, three ceramic systems were tested. The metal-ceramic (MCR) with 0.5 mm core and 1 mm Ceramco lll veneering layer; the all ceramic (CE) with 0.5 mm Cercon core and 1 mm Cercon Ceram veneering layer, and the all porcelain (VM7) with 1.5 mm Vita VM7. For each system, 15 disks (1.5mm×10mm) were made in Vita A2 shade. L*a*b* coordinates of the samples were measured by spectrophotometer and the Color difference with Vita A2 shade was calculated. The data were analyzed using one way ANOVA and multiple Tukey tests.Results: The Color coordinates of the target shade was L*=72.45, a=1.59 and b*=14.11. The mean L*a*b* values for MCR were 72.61±0.38, 2.88±0.24, 15.51±0.64, for CE were 76.42±0.46, 2.77±0.14, 17.51±0.61, and for VM7 were 75.13±0.6, 2.15±0.19, 17.9±0.61, respectively. The difference between coordinates of each system with target shade was significant. Only for MCR group, DE was in acceptable range (1.98). For CE (D=5.38) and VM7 (D=4.72) groups, the difference was unacceptable.Conclusion: Under the condition of this study, the metal-ceramic system had the best and acceptable match to the target shade.

A condition that must be satisfied for digital printers in a Color management system is converting device-dependent Color values into device-independent Color values by a transfer model of high-quality performance. Usually, each transfer model is initially trained with some specified samples as a training set before being tested. In the present study, Color charts with different quantities of Color patches in an RGB space were selected and printed as training groups. The spectral reflectances of all samples in printed Color charts were measured and converted to an XYZ Color space under a standard observer/illuminant combination. Using a matrix method, the RGB to XYZ transformation matrices were obtained with the algebraic operation of different degrees of polynomials and the least-square errors for the training groups. Similar operations were also performed for the well-known ProfileMaker software, which is based on a look-up table method. Finally, the statistical values of Color difference (DE2000) were calculated between the actual XYZ from the printed Color charts, the predicted XYZ from the matrix method, and those predicted from the ProfileMaker software. The ProfileMaker software showed a significantly lower Color difference near the perception threshold for the specified training samples.

To reproduce an image, it is necessary to map out of gamut Colors of the image to destination gamut. It is clear that the best Color gamut mapping introduces the perceptually closest image to the original one. In this study, a new Color gamut mapping is purposed by the aid of Genetic Algorithm (GA). The Color difference between the original and mapped images based on SLAB formula was chosen as fitness function. The proposed algorithm was applied in CIELAB Color space and special genetic operators were developed to meet the aim of gamut mapping. To increase the rate of convergence and have a faster algorithm, one of the initial population chromosomes can be obtained from the result of clipping method. The results showed that the new method introduces smaller Color difference between the reproduced and original images in comparison with the common clipping method. The other advantage of the genetic Color gamut mapping is that any new criterion for Color image difference can be easily used as a fitness function. In addition, by this method the final Colors are not restricted to the gamut surface and they may be included into the gamut.