According to Berlin and Kay, there is a set of 11 universal basic color terms: White, Black, Red, Green, Yellow, Blue, Brown, Purple, Pink, Orange, and Gray. To approve this, a world survey (WCS) was begun to gather data on color categorization through the world, although, despite many languages, Persian language has not been studied so far. We report a study of color naming in Iran. A study was carried out in six major cities including: Tehran, Isfahan, Mashhad, Yazd, Rasht, and Shiraz to investigate the color naming for Persian language and also the effect of local culture. The main purpose of this study was to describe the basic color terms of Persian language and to verify Berlin and Kay's theory. In total, 200 subjects were studied from all the six cities. Our results showed that there were the same basic color terms (as Berlin and Kay's theory) in Persian language overall including:“ (White) ”,“ (Black) ”, “ (Red) ”, “ (Green) ”, “ (Yellow) ”, “ (Blue) ”, “ (Brown) ”, “ (Purple) ”, “ (Pink) ”, “ (Orange), ” and “ (Gray) ”. However, this was not valid for all the cities when they were investigated individually. There were 10 basic terms in Mashhad and Shiraz while the other cities had 11 basic terms. Also, the set of the used terms would differ by the city. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 352–360, 2015

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In recent years, various surface roughness measurement methods have been proposed as alternatives to the commonly used stylus profilometry, which is a low-speed, destructive, expensive but precise method. In this study, a novel method, called "image profilometry," has been introduced for nondestructive, fast, and low-cost surface roughness measurement of randomly rough metallic samples based on image processing and machine vision. The impacts of influential parameters such as image resolution and filtering approach for elimination of the long wavelength surface undulations on the accuracy of the image profilometry results have been comprehensively investigated. Ten surface roughness parameters were measured for the samples using both the stylus and image profilometry. Based on the results, the best image resolution was 800 dpi, and the most practical filtering method was Gaussian convolution+cutoff. In these conditions, the best and worst correlation coefficients (R) between the stylus and image profilometry results were 0.9892 and 0.9313, respectively. Our results indicated that the image profilometry predicted the stylus profilometry results with high accuracy. Consequently, it could be a viable alternative to the stylus profilometry, particularly in online applications.

To predict the perceived color differences, the effect of the surface texture on the performance of the color difference formulae was investigated. To this end, knitted polyester fabrics with eight different textures were prepared. The fabrics were dyed by seven dyestuffs in five different depths. The selected pairs from the five samples with different depths in each hue covered small to large color differences. The assessed pair of samples had the same texture and hue, but different depths. A panel of 23 observers assessed the color differences of the pairs by gray scale method. The results showed that for the textile samples with different texture structures, the CIEDE2000 (2:1:1) performed the best followed by CMC (2:1:1), CIE94 (2:1:1), and CIELAB with approximately same performance. In addition, the magnitude of color difference influenced the ability of the formulae to predict the visual assessments and the best performance obtained for medium color differences. The comparison between eight different texture groups indicated that the texture structures of the pairs significantly affected the performance of the color difference formulae. For instance, the PF/3 measures obtained for the eight texture groups by CIEDE2000 (2:1:1) color formula could be varied between 21.98 and 33.37 PF/3 units.

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Spectrophotometers are usually built based on one of these two kinds of geometry; d/8 (diffuse/8) and 45/0. Considering that measured or observed colors depend on the illuminating and viewing conditions, the 45/0 spectrophotometer geometry or d/8 geometry affects the results. In addition, the sample characteristics such as gloss or texture can also influence the measuring results. Therefore, the effect of geometry will be affected by the characteristics of the sample. In this paper, the effect of spectrophotometer geometry on the result of color measurement is investigated for textile samples with different types of texture. In addition, the effect of texture on the measured colors was compared for two geometries. To this end, 63 polyester samples contained of 9 color centers each of them has 7 different types of texture were used. The spectral reflectance data of the fabrics were measured using two spectrophotometers with different geometry; d/8 and 45/0. Other parameters of the two measuring systems such as the aperture size, laboratory conditions, the sample's direction in front of the spectrophotometer and the number of specimen's layers were equal. The measured values of two instruments were compared. It was shown that the spectrophotometer geometry influences the color coordinates of the samples. The d/8 spectrophotometer with include mode usually gives larger lightness values. C* and H* values are affected by the type of spectrophotometer and this effect is dependent on the color center of the sample. In another part of the present study, the color change via the texture difference was evaluated for each of the two geometries, and the obtained results were compared with each other. It was found that the surface texture of the sample influences the measured color from 0.4 to about 4.8 CIEDE2000 (1:1:1) unit for both two types of the spectrophotometer. Moreover, as same as d/8 geometry, the color change via the texture which is measured by 45/0 shows no acceptable correlation with visual assessments.