Research on the detection of fabric color difference based on T‐S fuzzy neural network

T‐S fuzzy neural network algorithm is used to establish the mapping relationship from the RGB space to the L*a*b* space, which avoids the complex process of color space conversion. Meanwhile, the block method is adopted to detect color difference of dyed fabric that is wide format and wide viewing angle. Color differences in different regions can be calculated with Color Measurement Committee color difference formula based on T‐S fuzzy neural network. Experimental results are in accordance with the spectrophotometer measurement, which proves that T‐S fuzzy neural network algorithm used in real‐time color detection process is effective and feasible. Workers can make corresponding adjustment on‐line according to the deviation to ensure the quality of fabric color and reduce the loss.     

Analysis of five sets of color difference data

In a systematic optimization process five sets of recent color difference data have been analyzed for commonalities. Adjustment of the X tristimulus values and application of a systematic, surround dependent S_L function was found to be beneficial in all cases. Other modifications of the CIE94 color‐difference formula were found to bring improvements only in some cases and may be spurious. Application of what seem to be nonsystematic scale factors in a range of 0.78–1.38 improve correlation between calculated and visual color differences in all cases. After optimization, calculated color difference values explain between 80–90% of the variation in visual color differences. Some of the datasets are shown not to be well suited for formula optimization. Optimization in all cases by set, for three sets of data by quadrant in the a^*b^* diagram, and for one set by subset did not reveal any additional systematic trends for improvement. It appears that the basic structure of CIE94, with the recommended modifications, is a good approximation as a model for color‐difference evaluation in the range from 0.5–10 units of difference. The model is surround dependent. A number of issues remain to be resolved. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 141–150, 2001     

A novel method for color determination of edible oils in L*a*b* format

A simple method that uses visible spectrophotometer data and an artificial neural network (ANN) was developed to determine edible oil color based on the L*a*b* format. The 100 oil samples consisted of nine pure oils, a sesame oil blend and three heated oils. Binary, ternary and quaternary mixtures of these 13 oils in different ratios were prepared, and absorbance values of the samples were measured in the visible region (380–700 nm). The absorbance values at wavelengths of 416, 456, 483, 537, 611 and 672 nm were used to train, validate and test the network. Strong correlations between the instrumental L*a*b*ΔE and the estimated L*a*b*ΔE were found for the test samples, with correlation coefficients (R²) of 0.989, 0.984, 0.996 and 0.992 for L*, a*, b*, and ΔE, respectively. The effects of number and combination of the wavelengths used for training of the ANN on the estimation capability of the network for the test samples were also investigated. Although a good agreement, average R² of 0.991– 0 993 for L*a*b*, was obtained for combinations composed of three to six wavelengths with 483 and 537 nm in common, the best R² value was obtained when all six wavelengths were used to train the ANN. The developed method is objective, cost effective and simple, and allows the color measurement with a basic visible spectrophotometer and disposable cuvettes.     

A general form of color difference formula based on color discrimination ellipsoid parameters

A general color difference formula has been derived based on the parameters of color discrimination ellipsoids in the CIE 1976 L*a*b* color space. By using different orders of approximation, the general formula resembles the basic forms of the current formulae. The method described in this article suggests a framework for modifying the CIE 1976 L*a*b* color difference formula.     

Comparison of the metrics between the CIELAB and the DIN99 uniform color spaces using dental resin composite material values

The sizes for the perceptible or acceptable color difference measured with instruments vary by factors such as instrument, material, and color‐difference formula. To compensate for disagreement of the CIELAB color difference (ΔE^*_ab) with the human observer, the CIEDE2000 formula was developed. However, since this formula has no uniform color space (UCS), DIN99 UCS may be an alternative UCS at present. The purpose of this study was to determine the correlation between the CIELAB UCS and DIN99 UCS using dental resin composites. Changes and correlations in color coordinates (CIE L*,a*, and b* versus L₉₉, a₉₉, and b₉₉ from DIN99) and color differences (ΔE^*_ab and ΔE₉₉) of dental resin composites after polymerization and thermocycling were determined. After transformation into DIN99 formula, the a value (red–green parameter) shifted to higher values, and the span of distribution was maintained after transformation. However, the span of distribution of b values (yellow–blue parameter) was reduced. Although color differences with the two formulas were correlated after polymerization and thermocycling (r = 0.77 and 0.68, respectively), the color coordinates and color differences with DIN99 were significantly different from those with CIELAB. New UCS (DIN99) was different from the present CIELAB UCS with respect to color coordinates (a and b) and color difference. Adaptation of a more observer‐response relevant uniform color space should be considered after visual confirmation with dental esthetic materials. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 168–173, 2006     

Formulating Pantone colors by unused base inks,formulation software,and a spectrophotometer

Unused base inks that are not going to be used for printing production are considered to be hazardous materials. Their disposal is expensive, and strict environmental regulations should be followed for their disposal. As an alternative, this article describes how spectral data of unused base inks can be gathered and mixed to generate new colors to incorporate them back to print production for small‐volume jobs. In this study, 30 different Pantone colors were selected as target colors. The CIE L*a*b* spectral data of Pantone colors and unused base inks were gathered via a spectrophotometer. A commercial formulation software, based on multiflux theory and CIE L*a*b* color space, was used to formulate ink recipes that contained the base inks. To quantify the performance of ink recipes, they were mixed and printed using an offset printability tester. The CIELAB ΔE*_ab metric, developed by CIE, was used to detect the visual differences between the target Pantone Color and printed colors.     

Time‐temperature superposition principle application to the hygrothermal discoloration of colored high‐density polypropylene/wood composites

Discoloration kinetics of wood plastic composites (WPCs) made of recycled high density polyethylene (HDPE) during accelerated hygrothermal aging test at 45–65°C was conducted by means of time‐temperature superposition principle (TTSP). Color parameters measured with a spectrophotometer by CIELAB color system showed that L* and ΔE* increased while a* and b* decreased with aging time and temperature. A single horizontal shift was adequate for the superposition of color parameter master curves, indicating temperature can be used as accelerating factor to the discoloration of the studied WPCs. The regression line for Arrhenius plot showed a linearity for color parameters (R² = 0.9996), suggesting TTSP concept can be used to analyze and predict the discoloration kinetics of WPCs at low aging temperature. The prediction of color parameters at ambient aging temperature (21°C) can be extended to 7.2 years. The activation energy calculated from color parameters using TTSP method was 85.5 kJ/mol and similar to the reported values of HDPE thermal relaxation reactions calculated from other methods in the open literature. POLYM. COMPOS. 37:1016–1020, 2016. © 2014 Society of Plastics Engineers     

Testing the differences between two color measurement probability distributions using Hotelling's T2 test and the permutation test

It is common practice in statistics to test the equality of two population means using, for example, the Student's t test, in the univariate case, or the Hotelling's T² Test in the multivariate case. However, tests on the equality of population means are not well developed for testing the difference between two populations of color measurements. Methods for analyzing populations of spectral reflectance and L*a*b* measurements have been described for applications such as analyzing inter-instrument agreement and repeatability. Methods have also been proposed for the analysis of color differences, but there are little written about techniques for testing whether two samples have the same probability distribution. This article focuses on testing the difference between color measurement probability distributions based on color difference. In addition, a metric is proposed called the threshold for color difference discrimination (TCDD, in units of ΔE), the color difference at which two populations can be considered to have different population distributions. A lower TCDD means smaller color differences between two samples can be resolved. Two parametric tests based on Hotelling's T² test and a nonparametric permutation test were used to determine the TCDD for populations of color measurements with different variances and sample sizes. The TCDD was found to be smaller by tests using the Hotelling's T² statistic, compared with a permutation test performed directly on color difference. It was also found, as expected, that larger sample sizes led to smaller TCDDs, as did smaller population variances.     

Uniform colour spaces based on the DIN99 colour‐difference formula

Several colour‐difference formulas such as CMC, CIE94, and CIEDE2000 have been developed by modifying CIELAB. These formulas give much better fits for experimental data based on small colour differences than does CIELAB. None of these has an associated uniform colour space (UCS). The need for a UCS is demonstrated by the widespread use of the a*b* diagram despite the lack of uniformity. This article describes the development of formulas, with the same basic structure as the DIN99 formula, that predict the experimental data sets better than do the CMC and CIE94 colour‐difference formulas and only slightly worse than CIEDE2000 (which was optimized on the experimental data). However, these formulas all have an associated UCS. The spaces are similar in form to L*a*b*. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 282–290, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10066     

Geodesic calculation of color difference formulas and comparison with the munsell color order system

Riemannian metric tensors of color difference formulas are derived from the line elements in a color space. The shortest curve between two points in a color space can be calculated from the metric tensors. This shortest curve is called a geodesic. In this article, the authors present computed geodesic curves and corresponding contours of the CIELAB ( ), the CIELUV ( ), the OSA‐UCS (ΔE_E) and an infinitesimal approximation of the CIEDE2000 (ΔE₀₀) color difference metrics in the CIELAB color space. At a fixed value of lightness L*, geodesic curves originating from the achromatic point and their corresponding contours of the above four formulas in the CIELAB color space can be described as hue geodesics and chroma contours. The Munsell chromas and hue circles at the Munsell values 3, 5, and 7 are compared with computed hue geodesics and chroma contours of these formulas at three different fixed lightness values. It is found that the Munsell chromas and hue circles do not the match the computed hue geodesics and chroma contours of above mentioned formulas at different Munsell values. The results also show that the distribution of color stimuli predicted by the infinitesimal approximation of CIEDE2000 (ΔE₀₀) and the OSA‐UCS (ΔE_E) in the CIELAB color space are in general not better than the conventional CIELAB (ΔE) and CIELUV (ΔE) formulas. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 259–266, 2013     

彩色涤纶短纤维色差的控制

介绍了用于评定色差的CIELAB和CMC色差公式,并对2个公式在彩色涤纶短纤维色差控制中的实际运用做了对比实验。从对比结果和数据分析来看,CIELAB色差公式存在一定的局限性,CMC色差公式测试结果与目测具有更好的视觉一致性,完全可以在纺织行业色差控制上替代CIELAB色差公式并推广使用。     

Investigation on new color depth formulas

Color depth is difficult to evaluate; however, it plays an important role in the assessments of color fastness, dyeing properties, and so on. The subjective evaluation of color depth is prone to be affected by people, environment, etc. As for objective evaluation, there are more than 10 formulas, which confuses the user. In this study, a theoretically designed new formula is inspected through 18195 chips with 24 grades of color depth from the SINO COLOR BOOK, with the help of four preferable objective evaluation formulas. The specimens were measured using an X‐Rite Color i7 spectrophotometer, and all their depth values were calculated and statistically analyzed by programming MATLAB. Of the five formulas, the new formula yields the best outcome of variance coefficients (CVs) but the worst linearity, with a correlation coefficient R = 0.976. It was then theoretically revised to two other formulas, one obtains the highest linearity (R = 0.9997) and the third CV, and the other gains the second linearity (R = 0.9984) and the second CV among the seven formulas. Besides, the three new formulas are not as sensitive as the others to the changes of Hue and Chroma. In general, the new revised formulas show potential and need to be further evaluated.     

Evaluation of performance of various color‐difference formulae using an experimental black dataset

The objective of this study was to develop a specific visual dataset comprising black‐appearing samples with low lightness (L* ranging from approximately 10.4 to 19.5), varying in hue and chroma, evaluating their visual differences against a reference sample, and testing the performance of major color difference formulas currently in use as well as OSA‐UCS‐based models and more recent CAM02 color difference formulas including CAM02‐SCD and CAM02‐UCS models. The dataset comprised 50 dyed black fabric samples of similar structure, and a standard (L*= 15.33, a* = 0.14, b* = ?0.82), with a distribution of small color differences, in ΔE^*_ab, from 0 to approximately 5. The visual color difference between each sample and the standard was assessed by 19 observers in three separate sittings with an interval of at least 24 hours between trials using an AATCC standard gray scale for color change, and a total of 2850 assessments were obtained. A third‐degree polynomial equation was used to convert gray scale ratings to visual differences. The Standard Residual Sum of Squares index (STRESS) and Pearson's correlation coefficient (r), were used to evaluate the performance of various color difference formulae based on visual results. According to the analysis of STRESS index and correlation coefficient results CAM02 color difference equations exhibited the best agreement against visual data with statistically significant improvement over other models tested. The CIEDE2000 (1:1:1) equation also showed good performance in this region of the color space. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 589–598, 2014     

Color effect of light sources on peridot based on CIE1976 L*a*b* color system and round RGB diagram system

Previous research indicated that the peridot's color is dominated by the selective absorption of visible light caused by ferrous ion, the hue angle of which is in an inverse ratio of the concentration of Fe²⁺. This article focuses on the color effect of peridot under different standard light sources based on the CIE1976 L*a*b* color space system and round RGB diagram system and tries to find the best one for its grading and display. Based on the results of a series of experiments, including electron microprobe analysis, spectrophotometer, UV‐Vis spectrum, standard illumination box, and Munsell neutral color chips, it was suggested that the spectral power distribution and color temperature of a standard light source significantly influence the color of peridot in terms of lightness and chroma, particularly in the hue of peridot. As for color grading and displaying of peridot, standard light source A fails to fit in, and the color of peridot under a fluorescent light source has a higher chroma but a lower hue angle than that under daylight light source. The best choice for grading and displaying peridot is the standard light source D₆₅. It is better to distinguish the hue of peridot when it is calculated by the round RGB diagram system.     

Are chroma tolerances dependent on hue‐angle?

Relationships between suprathreshold chroma tolerances and CIELAB hue‐angles have been analyzed through the results of a new pair‐comparison experiment and the experimental combined data set employed by CIE TC 1–47 for the development of the latest CIE color‐difference formula, CIEDE2000. Chroma tolerances have been measured by 12 normal observers at 21 CRT‐generated color centers L*₁₀ = 40, C*_ab,10 = 20 and 40, and h_ab,10 at 30° regular steps). The results of this experiment lead to a chroma‐difference weighting function with hue‐angle dependence W_CH, which is in good agreement with the one proposed by the LCD color‐difference formula [Color Res Appl 2001;26:369–375]. This W_CH function is also consistent with the experimental results provided by the combined data set employed by CIE TC 1–47. For the whole CIE TC 1–47 data set, as well as for each one of its four independent subsets, the PF/3 performance factor [Color Res Appl 1999;24:331–343] was improved by adding to CIEDE2000 the W_CH function proposed by LCD, or the one derived by us using the results of our current experiment together with the combined data set employed by CIE TC 1–47. Nevertheless, unfortunately, from the current data, this PF/3 improvement is small (and statistically nonsignificant): 0.3 for the 3657 pairs provided by CIE TC 1–47 combined data set and 1.6 for a subset of 590 chromatic pairs (C*_ab,10>5.0) with color differences lower than 5.0 CIELAB units and due mainly to chroma. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 420–427, 2004; Published online in Wiley Interscience (www.interscience.wiley.com). DOI 10.1002/col.20057     

Quantitative representation of floral colors

Human and insect pollinator perceived floral colors of 81 species of angiosperms (flowering plants) from Trivandrum (Kerala, India) was represented using the CIE 1976 L*a*b* color space and color hexagon, respectively. The floral color difference among human perceived red, yellow, and blue‐hued flowers and that of each flower from its respective pure hue was calculated using the CIE ΔE 2000 formula. Human perceived floral color difference values were consistently higher than 3.5, indicating the uniqueness of floral colors. Flowers perceived red and yellow by humans were dominant and of comparable proportions. Insect pollinators perceive most of the flowers as blue‐green. Quantitative representation of human and pollinator perceived floral colors would be invaluable to understand the information broadcasted by flowers. It can form the basis of flower grading in the floriculture industry and underpin objectivity in evolving the framework for national pollinator strategies.     

Correspondence Between CIELAB and CIELUV Color Differences

The CIE presently recommends two uniform color spaces, the CIE 1976 (L*u*v*)-space (CIELUV) and the CIE 1976 (L*a*b*)-space (CIELAB). With each of these spaces is associated a color-difference formula. Color differences calculated by one formula cannot readily be converted to color differences calculated by the other formula. A conversion factor such as ρ = ΔE_uv*/ΔE_ab* cannot be determined uniquely. However, for any given location in color space, it is possible to determine a range, ρ_min ? ρ ? ρ_max, within which ρ must lie. Lines of constant ρ_min and ρ_max can be plotted in (L*a*b*)-space which indicate the range of ρ in (L*u*v*)-space.     

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

Development of a comprehensive visual dataset based on a CIE blue color center: Assessment of color difference formulae using various statistical methods

The objectives of this work were to develop a comprehensive visual dataset around one CIE blue color center, NCSU‐B1, and to use the new dataset to test the performance of the major color difference formulae in this region of color space based on various statistical methods. The dataset comprised of 66 dyed polyester fabrics with small color differences ($\Delta E_{{\rm ab}}^* < 5$ ) around a CIE blue color center. The visual difference between each sample and the color center was assessed by 26 observers in three separate sittings using a modified AATCC gray scale and a total of 5148 assessments were obtained. The performance of CIELAB, CIE94, CMC(l:c), BFD(l:c), and CIEDE2000 (K_L:K_C:K_H) color difference formulae based on the blue dataset was evaluated at various K_L (or l) values using PF/3, conventional correlation coefficient (r), Spearman rank correlation coefficient (ρ) and the STRESS function. The optimum range for K_L (or l) was found to be 1–1.3 based on PF/3, 1.4–1.7 based on r, and 1–1.4 based on STRESS, and in these ranges the performances of CIEDE2000, CMC, BFD and CIE94 were not statistically different at the 95% confidence level. At K_L (or l) = 1, the performance of CIEDE2000 was statistically improved compared to CMC, CIE94 and CIELAB. Also, for NCSU‐B1, the difference in the performance of CMC (2:1) from the performance of CMC (1:1) was statistically insignificant at 95% confidence. The same result was obtained when the performance of all the weighted color difference formulae were compared for K_L (or l) 1 versus 2. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2011