CIELAB Space Research Articles

Florence "blues" are clothed in triple basic terms.

Psycholinguistic studies provide evidence that Italian has more than one basic color term (BCT) for “blue”: consensually, blu denotes “dark blue,” while “light-and-medium blue,” with diatopic variation, is termed either azzurro or celeste. For Tuscan speakers (predominantly from Florence), the BLUE area is argued to linguistically differentiate between azzurro “medium blue” and celeste “light blue.” We scrutinized “basicness” of the three terms. Participants (N = 31; university students/graduates born in Tuscany) named each chip of eight Munsell charts encompassing the BLUE area (5BG-5PB; N = 237) using an unconstrained color-naming method. They then indicated the “best exemplar” (focal color) of blu, azzurro and celeste. We found that frequencies of the three terms and of term derivatives were comparable. Referential meaning of blu, azzurro, and celeste was estimated in CIELAB space as L*a*b*-coordinates of the mean of focal colors and as “modal” categories, that is, dispersion around the mean. The three “blue” terms were distinct on both measures and separated along all three CIELAB dimensions but predominantly along the L*-dimension. Our results provide evidence that Tuscan speakers require all three terms for naming the BLUE area, categorically refined along the lightness dimension. Furthermore, celeste appears to be a third BCT for “blue,” along with commonly considered BCTs azzurro and blu. The “triple blues” as BCTs for Tuscan speakers are in contrast with outcomes of two “blue” basic terms estimated by using the same methodology in two other locations in Italy—azzurro and blu (Verona, Veneto region) or celeste and blu (Alghero, Sardinia).

A new approach to broaden the range of eye colour identifiable by IrisPlex in DNA phenotyping

IrisPlex system represents the most popular model for eye colour prediction. Based on six polymorphisms this model provides very accurate predictions that strongly depend on the definition of eye colour phenotypes. The aim of the present study was to introduce a new approach to improve eye colour prediction using the well-validated IrisPlex system. A sample of 238 individuals from a Southern Italian population was collected and for each of them a high-resolution image of eye was obtained. By quantifying eye colour variation into CIELAB space several clustering algorithms were applied for eye colour classification. Predictions with the IrisPlex model were obtained using eye colour categories defined by both visual inspection and clustering algorithms. IrisPlex system predicted blue and brown eye colour with high accuracy while it was inefficient in the prediction of intermediate eye colour. Clustering-based eye colour resulted in a significantly increased accuracy of the model especially for brown eyes. Our results confirm the validity of the IrisPlex system for forensic purposes. Although the quantitative approach here proposed for eye colour definition slightly improves its prediction accuracy, further research is still required to improve the model particularly for the intermediate eye colour prediction.

Deep Learning Application in Detecting Glass Defects with Color Space Conversion and Adaptive Histogram Equalization

Manually detecting defects on the surfaces of glass products is a slow and time-consuming process in the quality control process, so computer-aided systems, including image processing and machine learning techniques are used to overcome this problem. In this study, scratch and bubble defects of the jar, photographed in the studio with a white matte background and a -60° peak angle, are investigated with the Yolo-V3 deep learning technique. Obtained performance is 94.65% for the raw data. Color space conversion (CSC) techniques, HSV and CIE-Lab Luv, are applied to the resulting images. V channels select for preprocessing. While the HSV method decreases the performance, an increase has been observed in the CIE-Lab Luv method. With the CIE-Lab Luv method, to which is applied the adaptive histogram equalization, the maximum recall, precision, and F1-score reach above 97%. Also, Yolo-V3 compared with the Faster R-CNN, it is observed that Yolo-V3 gave better results in all analyzes, and the highest overall accuracy is achieved in both methods when adaptive histogram equalization is applied to CIE-Lab Luv.

New software for comparing the color gamuts generated by printing technologies

In the color industry, it is vital to know the color gamut of a given device. Several tools for visualizing and comparing color gamuts are available but they each have some drawbacks. Therefore, the aim of this work was to develop and validate new software for comparing the color gamuts generated by printing devices; we also developed an automated color measurement system. The software simultaneously represents the gamuts in the 3D CIELAB space. It also calculates the Gamut Comparison Index and the volume using two algorithms (Convex Hull and Alpha Shapes). To evaluate the performance of our software, we first compared the results it obtained for the color gamuts with those from other comparison methods such as representation in the CIE 1931 chromaticity diagram or other color spaces. Next, we used Interactive Color Correction in 3 Dimensions (ICC3D) software to compare the gamut representations and volumes. Our software allowed us to identify differences between color gamuts that were not discriminated by other methods. This new software will enable the study and comparison of gamuts generated by different printing technologies and using different printing substrates, International Color Consortium profiles, inks, and light sources, thereby helping to achieve high quality color images.

Determining the ideal initial printing colorants in electrophotography by the discrete gradation trajectories

The accuracy and repeatability of the color reproduction in print is determined by the fine‐tuning of the tone reproduction curves of the basic printing colorants (most often this is CMYK). However, the diversity of manufacturers of printing equipment and dyes introduces an element of significant uncertainty about color uniformity. In addition, the traditional approach does not take into account the effect of hue change when applying the original dyes, as well as the nonlinearity of the hue rise in high‐ and low‐density areas. Determining the color of base colorants that produces the most uniform tone change is an important engineering challenge. Previously, there was no scientific basis for such calculations. We recently proposed an alternative color correction model based on gradation trajectories as an analogue of gradation curves in the CIE Lab space. We have also described the extension of the approach to double color overlay (gradation surfaces) and its analytical and discrete implications. The trajectories are the geodetic lines on gradation surfaces. In this paper, we propose using the gradation trajectories to determine “ideal” or “true” initial printing dyes for electrophotography. To simplify calculations, natural color discretization in digital printing is used.

Application of betacyanins pigments from Alternanthera brasiliana as yogurt colorant

Betacyanins are betalain derivatives showing a red-violet color suitable as a natural food colorant. An aqueous extract rich in betacyanin pigments and phenolic compounds from Alternanthera brasiliana was characterized using a high-performance liquid chromatography system with diode array and electrospray mass spectrometry detection. The major betacyanins (amaranthine, isoamaranthine, betanin and isobetanin, celosianin II and isocelosianin II) were identified and quantified. Eleven peaks were analyzed for phenolic compounds grouped into hydroxycinnamic acids and flavones. Different plant extract concentrations were added to freshly prepared set type yogurts (350, 700, and 1400 μL/80 g yogurt). Color properties in CIELAB space, pH, total phenolic content, and ferric reducing antioxidant power were analyzed on yogurts during storage time. The evolution of pigments and polyphenolic compounds during cold storage was studied by HPLC-ESI-QTOF-MS screening.The extracted color showed to be stable at the yogurt pH range. When added to yogurts, a natural berry-like color was achieved. These results provide a natural plant pigment-based alternative for application in food products, being the first time that A. brasiliana is used as a colorant in a dairy product. Besides color, polyphenols input turns Alternanthera brasiliana extract into a desirable functional food ingredient.

Automatic Gemstone Classification Using Computer Vision

This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local binary pattern, Haralick texture and grey-level co-occurrence matrix properties) were used in combination with different machine-learning algorithms (Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour, Decision Tree, Random Forest, Naive Bayes and Support Vector Machine). Deep-learning classification with ResNet-18 and ResNet-50 was also investigated. The optimal combination was provided by a Random Forest algorithm with the RGB eight-bin colour histogram and local binary pattern features, with an accuracy of 69.4% on unseen images; the algorithms required 0.0165 s to process the 284 test images. These results were compared against three expert gemmologists with at least 5 years of experience in gemstone identification, who obtained accuracies between 42.6% and 66.9% and took 42–175 min to classify the test images. As expected, the human experts took much longer than the computer vision algorithms, which in addition provided, albeit marginal, higher accuracy. Although these experiments included a relatively low number of images, the superiority of computer vision over humans is in line with what has been reported in other areas of study, and it is encouraging to further explore the application in gemmology and related areas.

Quantifying the effects of feature similarity on attentional selection using psychophysical scaling

The similarity between target and distractor features is a main factor determining the efficiency of attentional selection. Prior studies investigating the impact of target-distractor similarity on performance have mostly selected similarity levels in a categorical and qualitative way (e.g., by using discrete color or shape categories), or have used distance in physical stimulus space to quantify similarity. However, findings from psychophysics have shown that the physical stimulus space does not map linearly to psychological similarity. Thus, we here assessed psychological similarity using psychophysical scaling methods and tested how these similarity functions relate to attentional selection. We measured psychological similarity of colors chosen from CIELab space as a test case. Then, using a standard visual search task, we measured search times using a wide range of target-distractor similarity levels (Exp. 1) and also manipulated the number of distractors (2 or 7) to measure search efficiency (Exp. 2-3). In another sustained attention task (Exp. 4), participants continuously monitored a set of target dots among distractor dots to detect brief changes in luminance, and the color similarity between target and distractor dots was manipulated across trials, as in the visual search tasks. We found that performance in both tasks was comparably affected by target-distractor similarity, with search RTs and luminance discriminability plateauing at approximately 40°-50° around the CIELab color wheel. In contrast, the number of visual search distractors only affected performance when similarity was very high (below 20°). Interestingly, performance in each task was non-linearly related to both the physical and psychological similarity between targets and distractors. Overall, these findings suggest that there is no simple linear relationship between feature similarity and attentional selection, and point to the importance of understanding how the underlying perceptual organization of a given feature space interacts with mechanisms of attentional selection.

Measurement of Gem Colour Using a Computer Vision System: A Case Study with Jadeite-Jade

This paper introduces computer vision systems (CVSs), which provides a new method to measure gem colour, and compares CVS and colourimeter (CM) measurements of jadeite-jade colour in the CIELAB space. The feasibility of using CVS for jadeite-jade colour measurement was verified by an expert group test and a reasonable regression model in an experiment involving 111 samples covering almost all jadeite-jade colours. In the expert group test, more than 93.33% of CVS images are considered to have high similarities with real objects. Comparing L*, a*, b*, C*, h, and ∆E* (greater than 10) from CVS and CM tests indicate that significant visual differences exist between the measured colours. For a*, b*, and h, the R2 of the regression model for CVS and CM was 90.2% or more. CVS readings can be used to predict the colour value measured by CM, which means that CVS technology can become a practical tool to detect the colour of jadeite-jade.

Characterizing skin color before and after 100‐m sprinting

AbstractWe studied skin color in participants before and after a 100‐m sprint using a Konica Minolta CM‐2600d spectrophotometer. Four body positions (forehead, cheek, neck, and inner forearm) were measured for the approximately 30 students (non‐professional runners) who participated in the experiment. Skin reflectance was measured three times at each position before running and again soon afterward. Analysis of the data showed that average measurement repeatability (mean color difference from the mean [MCDM]) was 0.5, 0.4, 0.3, and 0.2 CIELAB units for forehead, cheek, neck, and inner forearm, respectively. However, average skin color differences produced by running were 1.8, 2.2, 2.0, and 1.7 CIELAB units for forehead, cheek, neck, and inner forearm positions, respectively, which are considerably higher than repeatability before or after running. Furthermore, appearance variation was analyzed in the CIELAB space, and it was found that sprinting for 100 m considerably changes the values of the red‐green a* coordinate, while the lightness L* and yellow‐blue b* coordinates remain almost constant. More specifically, on average, a* decreases after running for the forehead, cheek, and neck positions, while the opposite is true for the inner forearm position. The findings in this article may be useful to test previous physical models, achieve more realistic images of human skin after sprinting, and contribute to work of CIE TC 1‐92 on skin spectra database.

Naturalness and aesthetics of colors – Preference for color compositions perceived as natural

What makes a colored image, e.g. an abstract painting or a landscape, look pleasing? We hypothesized that a preference for complex color compositions, such as paintings and images of natural scenes, might be related to how natural the colors are perceived. We tested this possibility with two experiments in which the degree of naturalness of images was manipulated by rotating their color gamut rigidly in the color space CIELAB. This changed just the hue composition, but preserved saturation and lightness. In the first experiment we obtained individual scaling curves for perceived naturalness and for preference as a function of the angle of gamut rotation for a small set of images. The naturalness and preference scaling curves were found to be largely similar and their maxima were close to the original image. In the second experiment, we tested whether this effect generalized to a larger set of images. We used a simultaneous 5AFC procedure where in each trial participants had to select the most natural or the most preferred image from five different rotations of the color gamut. The results confirmed the first experiment and showed that, in general, the images perceived as the more natural tend to be the ones that are preferred. Together these results show that perceived naturalness and preference are indeed perceptually closely related and may be driven by related mechanisms.

Liking of art and the perception of color.

Color has been a defining feature of paintings throughout art history. Despite the great diversity in the use of color between epochs, there are some surprisingly stable and unifying features in chromatic properties across visual artworks. For example, artists' palettes seem to be biased toward the yellow-red range of the spectrum. Here, we assess the impact of a holistic color manipulation (i.e., rotating the color gamut) on aesthetic liking and perceived colorfulness of abstract paintings. We presented 6 versions each of 100 abstract artworks that differed only in the rotational degree of their color gamut within the CIELAB space. Results indicated a very stable preference for the original color compositions-both on a participant level and on an item level. Furthermore, participants perceived original color compositions as more colorful than rotated versions. This effect remained robust even when the exact number of different colors-among other chromatic features-was taken into account in covariate analyses. Thus, it seems that original color compositions are inherently special. Specifically, it seems that the aesthetic appeal of original artworks arises from nontrivial color features, which are characterized by their distribution within the visible spectrum. We assume that the rotation manipulation may change the perception of some colors more strongly than others due to differences in sensitivity of our visual system to these hues. We discuss these findings with respect to category-specific color perception, which may be a potential contender for a neurobiological foundation of the observed effects. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Is color data from citizen science photographs reliable for biodiversity research?

Color research continuously demands better methods and larger sample sizes. Citizen science (CS) projects are producing an ever‐growing geo‐ and time‐referenced set of photographs of organisms. These datasets have the potential to make a huge contribution to color research, but the reliability of these data need to be tested before widespread implementation.We compared the difference between color extracted from CS photographs with that of color extracted from controlled lighting conditions (i.e., the current gold standard in spectrometry) for both birds and plants. First, we tested the ability of CS photographs to quantify interspecific variability by assessing > 9,000 CS photographs of 537 Australian bird species with controlled museum spectrometry data. Second, we tested the ability of CS photographs to quantify intraspecific variability by measuring petal color data for two plant species using seven methods/sources with varying levels of control.For interspecific questions, we found that by averaging out variability through a large sample size, CS photographs capture a large proportion of across species variation in plumage color within the visual part of the spectrum (R2 = 0.68–0.71 for RGB space and 0.72–0.77 for CIE‐LAB space). Between 12 and 14 photographs per species are necessary to achieve this averaging effect for interspecific studies. Unsurprisingly, the CS photographs taken with commercial cameras failed to capture information in the UV part of the spectrum. For intraspecific questions, decreasing levels of control increase the color variation but averaging larger sample sizes can partially mitigate this, aside from particular issues related to saturation and irregularities in light capture.CS photographs offer a very large sample size across space and time which offers statistical power for many color research questions. This study shows that CS photographs contain data that lines up closely with controlled measurements within the visual spectrum if the sample size is large enough, highlighting the potential of CS photographs for both interspecific and intraspecific ecological or biological questions. With regard to analyzing color in CS photographs, we suggest, as a starting point, to measure multiple random points within the ROI of each photograph for both patterned and unpatterned patches and approach the recommended sample size of 12–14 photographs per species for interspecific studies. Overall, this study provides groundwork in analyzing the reliability of a novel method, which can propel the field of studying color forward.

Evaluation of the quality and shelf-life of cayenne (Capsicum spp.)

A genetic characterization of 13 species of cayenne provided by Spanish milling industries was performed, and their colour, pungency and antioxidant properties were evaluated. Colour parameters (carotenoids, reflected and extractable colour), capsaicinoids, total phenolic compounds and antioxidant activity by the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging method and 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) were determined in the cayenne samples, and the stability of cayenne properties was determined after exposure to UV light. Genetic characterization using different barcode markers (26S large subunit of rDNA, internal transcribed spacer 2, rbcL gene and matK gene) revealed close relationships among samples. However, notable differences were found for most of the quality parameters studied. Specifically, colour parameters were the most important traits for the discrimination of different samples. Samples with high colour quality presented 989.9 μg of capsanthin/g of dry matter, 104.8 ASTA units and 26.6 units in the a* coordinate of the CIELAB space. In contrast, the lowest-quality sample presented 582.7 μg of capsanthin/g of dry matter, 21.1 ASTA units and 15.4 units in the a* coordinate. DPPH and ABTS values ranged from 73.8 to 161.8 and from 217.1 to 325.7 mg Trolox/100 g, respectively. Pungency was highly variable among samples, with values ranging between 68.2 and 751.9 μg of capsaicin/g and 38.7 and 273.5 μg of dihidrocapsaicin/g. Principal component analysis indicated that capsaicinoids and total phenolic compounds are the best indices to determine the antioxidant activity of cayenne, which is highly correlated with the stability of colour and pungency. According to these findings, the desirable technological qualities and the longest shelf-life of cayenne are associated with a high content of capsaicinoids and phenolic compounds, which guarantee good colour stability and pungency associated with high antioxidant activity.

Gray Balance Adjusting in Electrophotography by Means of Discrete Geodesics of Gradation Surfaces

Gray balance is one of the key issues of color balance and color appearance adjustment during printing. It takes into account the overlap of two or more colorants to achieve a visually neutral tone. It works in parallel with the adjustment of the main color channels. In electrophotography, the setting of color channels separately is usually carried out using tone reproduction curves. However, this traditional approach does not take into account the effect of changes in shades caused by their overlapping, We previously suggested using gradation trajectories (an analogue of tone reproduction curves in CIE Lab space) as an alternative paradigm for color channel adjustment. Further, we expanded the idea to the gradation surfaces as an expression of binary overlays. We proposed both analytic and discrete approaches to the computation of gradation trajectories and surfaces. The computations use the mathematical apparatus of differential geometry of spatial curves and surfaces. Discretization uses color quantization in digital printing devices and means that color coordinates are considered as continuous functions of filling a discrete raster cell with dyes. The color space metric is determined by the value of the CIE dE color difference. As gradation trajectories, we apply the discrete type of geodesic lines on the gradation surfaces of the corresponding binaries. In this work, we suggest using gradation surfaces in discrete form as a mathematical model of double dye overlays to adjust the gray balance in electrophotography.

Ripeness stage characterization of coffee fruits (coffea arabica L. var. Castillo) applying chromaticity maps obtained from digital images

Abstract The harvesting is one of the most expensive stages during Colombian coffee production since the selectivity of ripe fruits (from the trees) is carried manually nowadays. However, the scarcity of workforce has become a new challenge for developing technologies that can be used in situations that traditional mechanized harvesters cannot be implemented. The classification of coffee fruits through the ripeness could help create selective harvesting methods from the different engineering fields (robotic, artificial vision, vibratory systems, etc.). This study describes a methodology to characterize the coffee fruit ripeness (Coffea arabica var. Castillo) using color maps obtained from digital image processing and colorimetry. Ninety-two samples were measured, and for each sample, a digital image was processed to extract the most representative color with an algorithm (RGB space), and parallel to this, the chromaticity was measured with a colorimeter (CIELab space). Both color spaces were related through linear transformations computed from the data. Results show that the ripeness classification is well defined in both color spaces; however, RGB space was divided into four ripening since the ripeness timeline was linearly represented. Through the transformations, the image data were contrasted with those measured with the colorimeter, which demonstrated that it could be converted in a colorimeter under certain specific conditions.

Study of color kinetics of banana (Musa cavendish) under microwave drying by application of image analysis

Microwave drying works on the volumetric heating concept promoted by electromagnetic radiation at 0.915 or 2.450 GHz. In this study, banana (Musa Cavendish) was taken as the sample and treated under microwave drying. The effect of two process variables, namely slice thickness (2, 3.5, and 5 mm) and microwave power (180 W, 360 W, and 540 W), were studied on drying kinetics and color kinetics. It was observed that the inverse variation relationship exists between drying time and microwave power level while drying time and slice thickness exhibited a direct variation relationship. A Computer Vision System (CVS) was developed to measure the color values of banana in CIELab space using an algorithm written in MATLAB software. Once the color parameters were obtained, they were fitted in First and Zero-order kinetic models. Both models were found to describe the color values adequately. This study concludes that microwave drying is a promising dehydration technique for banana drying that reduces the significant time of drying. Application of CVS is an excellent approach to measure the surface color of banana.

How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?-A Study Based on Hyperspectral Imaging.

RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ΔE*ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ΔE*ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios.

Linear discriminant analysis in red sorghum using artificial intelligence

Red Sorghum landraces are predominantly cultivated in southern districts of Tamil Nadu, India and are noted for its anthocyanin (3-deoxyanthocyanin) content, high antioxidant activity, low glycemic index, dietary fiber, astringency, fodder value, high protein, biotic and abiotic stress resistance. In this study, the seeds of 72 sorghum landraces collected from Tamil Nadu were analyzed to measure morpho-colourimetric data using an image analyser. Artificial Intelligence is widely used in genetic research for its uncompromised accuracy and speed. The observed phenotypic data is used to create a statistical database and analysed by linear discriminate analysis (LDA) to identify and discriminate landraces for utilization in breeding projects. The classification is based on sorghum descriptors established by Indian Institute of Millet Research, Hyderabad. The LDA for the trait seed size had discriminated the landraces into three groups with an accuracy of 91.83%. Five groups were observed based on grain color Red Green–Blue colour space Model and CIELAB (Commission International de I’Elcairage Lightness Red/Green values Blue/Yellow values) colour space with accuracy 53.06%. The result showed that discrimination of landraces was possible with great accuracy by image analyser. The image analysis of seeds is found to be more effective, easy, non-destructive and fast method to classify landraces. This new phenomic approach is proved to be an important tool in the early selection of genotypes and facilitates speed breeding in colored sorghum.

An independent contribution of colour to the aesthetic preference for paintings

Walking around an art museum we can see how colours influence our aesthetic preferences: many great works of art would not be as impressive in grey scales. Is the beauty of colours in abstract paintings anchored to the spatial composition of the paintings, or can it be preserved even with random spatial arrangements? To test whether colour can have an independent contribution to aesthetic appreciation, we asked participants to select the preferred image among pairs of colour-manipulated versions of the same painting. We changed hue, but preserved lightness and saturation, by rotating the colour volume around the L* axis in CIELAB space. To test the influence of the spatial structure, the images of the paintings were presented: (1) in their original format, (2) spatially scrambled but preserving the colour composition, and (3) in a control condition with both colour and spatial scrambling. Relative preference as a function of hue angle was obtained for the four paintings in their original and modified forms.For the original paintings, we found that participants generally preferred the colour angles that matched the original version of the paintings. Crucially, participants preferred the same colour distributions for spatially scrambled paintings as for the original paintings. For the control condition, there were no preferred colour configurations. This suggests that the aesthetic preference of colours in our abstract paintings is not anchored to particular spatial compositions, but is at least partly preserved even when the spatial composition is destroyed. Paintings thus can contain an aesthetic component that is exclusively related to colour.