Color Prediction Research Articles

Towards high-quality thermal infrared image colorization via attention-based hierarchical network

Colorization is an effective technology to improve the imaging quality of thermal infrared sensors, which is of great importance to environmental perception systems. Recently, colorization for thermal infrared images has realized obvious improvement with the development of deep learning. However, the performance of current designs is not adequate to achieve satisfied colorization quality. This is mainly because that thermal infrared image colorization requires the abilities of both color prediction and grayscale detail recovery, which poses severe challenges for feature extraction due to the poor quality of thermal infrared images. Furthermore, most of the current networks are based on generative adversarial network, suffering the risks of training instability and mode collapse. In this paper, we propose a high-quality colorization network for thermal infrared images. By leveraging hierarchical architecture, attention mechanism, and proposing a composite loss function, our network not only significantly improves the quality of colorized thermal infrared images but also achieves a speed of 32 fps on NVIDIA 2080Ti GPU platform. In evaluation experiments, PSNR and SSIM can be increased by at least 0.87 dB and 0.0277 compared with previous methods on publicly available datasets. Experimental results demonstrate that the proposed network can produce state-of-the-art colorized thermal infrared images both in objective metrics and subjective quality.

Color prediction of layered dental resin composites with varying thickness

ObjectiveThe aim of this study was to assess the accuracy of a Principal Components Analysis (PCA)-based method for reflectance reconstruction and color estimation of layered dental resin-based composites with different thicknesses. MethodBi-layered samples of different clinically relevant thicknesses were created using shades of VITAPAN Excell (VE), VITAPAN Dentine (VD) and VITA Physiodens (VP), combined with their corresponding enamel shades. Spectral reflectance of all samples was measured over a black background using a non-contact spectroradiometer with CIE 45∘∕0∘ geometry. Two different PCA-based models, built from two different configurations of known samples, were proposed to reconstruct the spectral data and color of unknown layered samples. Root Mean Square Error (RMSE), Goodness of Fit (GFC), as well as ΔE00 with corresponding 50:50% acceptability and perceptibly thresholds (AT and PT) were used as performance assessment. ResultsThe 5-samples training set approach provided an average RMSE < 0.015 and GFC > 0.999 when measured and predicted spectral reflectances were compared, while for the 9-samples training set, RMSE < 0.0098 and GFC > 0.9999 were obtained. The overall mean color differences obtained with the 5-samples training set approach were ΔE00 = 0.99 (AT% = 96.25% and PT% = 32.50%), while using the 9-samples training set resulted in lower overall mean color differences: ΔE00 = 0.50 (AT% = 99.22% and PT% = 83.87%). SignificanceWithin the framework of this study, the two proposed PCA-based configurations allow the prediction of the spectral reflectance of layered dental resin-based composites of different shades and thicknesses, with a high degree of accuracy.

Application Model Construction of Traditional Cultural Elements in Illustration Design under Artificial Intelligence Background

The sustainable development of modern illustration art needs to dig deep into traditional culture in content and, on the basis of spreading cultural forms and improving cultural influence, supplement its own creative forms. The influence of AI (artificial intelligence) on illustration design not only is manifested in the optimization of illustration design tools and the improvement of design efficiency, but also makes the illustration design methods more diverse and promotes new breakthroughs in the illustration design concept under the influence of new technologies. In this paper, combining the advantages of two activation functions, SoftSign and ReLU, a new activation function, SReLU, is constructed and applied to CNN (Convolutional Neural Network). The multimode property of color prediction is modeled by quantifying ab color channels in Lab color space, and the introduction of GAN (Generative Adversarial Network) structure can make the whole model show better generation effect and improve the generation ability of image style conversion network. Results. The experiment proves the effectiveness of the method with high-quality image visual effect.

Substrate Color Prediction through Active Monitoring of the Exhaustion Dyeing Process

Nowadays, the monitoring and control of the exhaustion dyeing processes is still done in a reactive and indirect fashion. The monitoring focusses on variables that influence the output of the dyeing process, such as temperature, pH and electrolytes concentration. This approach is more reactive than preventive, since it does not proactively detect deviations in the dyed substrate color that are caused by other process variables.The aim of this work is to develop a customized system that could actively monitor and predict the color of the dyed substrate after washing. This involves mathematical models and integration of a USB 4000 spectrophotometer and T300 dip probe made by Ocean Optics, which collects, analyzes and process the data from dyeing solutions. Validation tests performed with direct dyes and 100% cotton fabrics showed a good correlation with the real results, especially after 60 minutes of dyeing. For all the tested dyes, applied alone and combined, the color differences (ΔE94) between the measured and the predicted color were equal or less than 0.70 and 1.52 respectively.

Determining the color appearance of Helmholtz-Kohlrausch effect for self-emissive displays

The correlation between the measured luminance of a stimulus and its perceptual brightness, which varies with saturation and color, has been studied extensively. The increase in the colorfulness of the stimulus generates a Helmholtz-Kohlrausch (H-K) effect, which appears ‘bright’. This study aims to quantify the H-K effect of a highly saturated self-luminous display and to apply it to the CAM16 model. It also attempts to quantify the H-K effect by using the ratio of lightness and luminance values obtained from the modified CAM16 model. Future research objectives include the accurate prediction of color attributes to develop a model of high saturation displays in order to compare conventional displays.

Contributions of Surround Conditions on Color Appearance of Peridot Based on CAM16 Prediction

Peridot is the most abundant mineral on the earth and is widely used as gemstone in the world. In this paper, EMP analysis, UV-vis spectroscopy, and X-Rite SP62 spectrophotometer were used to study the chemical composition and color of the peridot. The CAM16 model was applied to predict the color appearance of peridot in different surrounding conditions. The chemical analysis and UV-vis spectra analysis showed there are seven narrow absorption bands at 414nm, 443nm, 481nm, 512nm, 557nm, 664nm and 684nm in peridot that should be assigned to the Fe chromophore. The color predictions indicated that the peridot will be brighter and more vivid when the luminance increases, which corresponds to Hunt Effect and Stevens Effect. Viewing surroundings have a great influence on the lightness and brightness in color appearance of peridot. CAM16 color appearance model is not suitable to predict the contributions of the light source on the color appearance of peridot due to the metameric effect. The windowing issue and the facets in peridot should be taken into consideration during the prediction calculation.

The HST Large Program on ω Centauri. V. Exploring the Ultracool Dwarf Population with Stellar Atmosphere and Evolutionary Modeling

Brown dwarfs can serve as both clocks and chemical tracers of the evolutionary history of the Milky Way due to their continuous cooling and high sensitivity of spectra to composition. We focus on brown dwarfs in globular clusters that host some of the oldest coeval populations in the galaxy. Currently, no brown dwarfs in globular clusters have been confirmed, but they are expected to be uncovered with advanced observational facilities such as the James Webb Space Telescope (JWST). In this paper we present a new set of stellar models specifically designed to investigate low-mass stars and brown dwarfs in ω Centauri—the largest known globular cluster. The parameters of our models were derived from iterative fits to Hubble Space Telescope photometry of the main-sequence members of the cluster. Despite the complex distribution of abundances and the presence of multiple main sequences in ω Centauri, we find that the modal color–magnitude distribution can be represented by a single stellar population with parameters determined in this study. The observed luminosity function is well represented by two distinct stellar populations having solar and enhanced helium mass fractions and a common initial mass function, in agreement with previous studies. Our analysis confirms that the abundances of individual chemical elements play a key role in determining the physical properties of low-mass cluster members. We use our models to draw predictions of brown dwarf colors and magnitudes in anticipated JWST NIRCam data, confirming that the beginning of the substellar sequence should be detected in ω Centauri in forthcoming observations.

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot-air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non-invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non-invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non-invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.

Color constancy with an optimized regularized random vector functional link based on an improved equilibrium optimizer.

In order to improve the accuracy of illumination estimation, this paper proposes a color constancy algorithm based on an improved equilibrium optimizer (IEO) to optimize the network structure parameters and common parameters of the regularized random vector functional link (RRVFL) at the same time. First, the initial search agent (population) of the equilibrium optimizer algorithm is generated through opposition-based learning, and the particles (individuals in the population) of the search agent are updated using the IEO algorithm. Compared with the completely randomly generated search agent, the method of initializing the search agent through the IEO algorithm has a better convergence effect. Then, each segment of the search agent is mapped to the corresponding parameters of the RRVFL, and the effective input weight and hidden layer bias are selected according to the node activation to generate the network structure, which can realize the simultaneous optimization of hyperparameters and common parameters. Finally, by calculating the output weight, the light source color prediction of the image under unknown illumination is performed, and the image is corrected. Comparison experiments show that the IEO-RRVFL color constancy algorithm proposed in this paper has higher accuracy and better stability than other comparison algorithms.

Reflectance and color prediction of dental material monolithic samples with varying thickness

ObjectiveTo assess accuracy of reflectance reconstruction and color estimation of different dental materials with varying thicknesses using Principal Component Analysis (PCA). MethodA1, A2, A3, A3.5, B2, C2 and D2 shades and 5 thicknesses (within 0.5–2.5 mm range) of Vita Suprinity (VS-PC) and Vitapan Dentine (VD), were used. Reflectance measurements were performed over black background using a non-contact spectroradiometer with CIE 45∘∕0∘ geometry. A PCA based algorithm was proposed to reconstruct spectral data and color of samples, using both extrapolation and interpolation approaches. Root Mean Square Error (RMSE), Goodness of Fit (GFC), correlation coefficient (R2) as well as ΔE00 with corresponding 50:50% acceptability and perceptibly thresholds (AT and PT) were used as performance assessment. ResultsThe interpolation approach provided an average RMSE = 0.01 and GFC > 0.999 when comparing predicted and measured spectral reflectances for both materials, while for the extrapolation approach RMSE = 0.02 and GFC > 0.999. Interpolation approach also resulted in lower overall mean color difference ΔE00 = 0.8 (ΔE00 = 0.9 for VS-PC and ΔE00 = 0.7 for VD), while using extrapolation approach resulted in higher overall mean color difference ΔE00 = 1.6, although below the AT (ΔE00 = 1.8 for VS-PC and ΔE00 = 1.5 for VD). Correlation values between predicted and measured spectral reflectances of R2 = 0.987 and R2 = 0.993 were globally obtained for VS-PC and VD, respectively. SignificanceThe proposed PCA-based algorithm is able to efficiently predict reflectance spectrum and color of monolithic samples of different dental materials with varying thickness. It can be used to optimize dental materials manufacturing processes and to improve chromatic accuracy of clinical dental restorations.

Optimisation of attribution processes using novel pixel-value based colour detection approach

Conventional CNN-based colour detection mechanisms are not optimal due to colour overlap, for example, the CNN model can find it difficult to distinguish shades of red and orange. The proposed approach is aimed at optimizing colour detection in attribution processes using a superior, objective and mathematical approach. The proposed approach allows you to detect a much larger range of shades of colour while increasing colour detection accuracy and reducing the time it takes to predict colour without model training. The proposed approach performs better than the conventional CNN-based supervised machine learning colour detection owing to its enhanced quantifiability, increased degree of quantisation of the colour palette (i.e., an improved colour range that can be detected) and lower the complexity of time. The approach employs fundamental concepts of image processing to isolate pixels that best represents the object’s colour from the region of interest (RoI), following which a mode filter is applied to the RGB pixel values of the pixels lying in the RoI to obtain a single set of [R, G, B] values that best represents the colour of the object. The mapping of this set of RGB values to the name of colour defined in a “Colour Taxonomy” is performed using Euclidean or Manhattan distance, depending upon the application. The accuracy of colour prediction is enhanced and computing time is greatly reduced. The current approach is aimed to be more accurate, more granular in detection (i.e., allows you to detect a much larger range of shades of colour and more time-effective than CNN-based colour detection and manual tagging thus greatly helping optimize attribution processes.

A multiple predictive tool approach for phenotypic and biogeographical ancestry inferences

Phenotypic and biogeographical ancestry predictions for 128 Canadians with various self-reported ancestries, generated using the ForenSeq™ DNA Signature Prep kit/Primer Mix B and VEROGEN’s Universal Analysis Software (UAS) v1.3, were compared to predictions derived from the ERASMUS v2.0, Snipper Application v2.5, Forensic Reference/Resource on Genetics knowledge base [FROG-kb] ©2019 and STRUCTURE v2.3.4, web tools. Performance metrics were calculated for all tools tested. The UAS v1.3 eye color predictions were determined to be accurate (91.8% for brown, 82.4% for blue) with no need to complement results with any other predictive tools. For hair color predictions, the UAS v1.3 was accurate for black (93.5%), for red (80.0%) but limited for brown (50.0%) and for blond (31.3%) using the Highest Probability Approach. The ERASMUS web tool could complement the UAS results using a revised Prediction Guide Approach. Non-admixed individuals with ancestries from Africa, East Asia or Europe were predicted with 79.7% accuracy using the UAS v1.3. Admixed individuals and those with ancestries from India, the Middle East and South America were better classified using Snipper, FROG-kb and STRUCTURE. Complementing UAS v1.3 predictions with those obtained from open access web tools thus represents a way to maximize information derivable from unknown samples.

Application of artificial intelligence techniques in textile wastewater decolorisation fields: A systematic and citation network analysis review

AbstractThis study reviewed 155 journal articles to examine how artificial intelligence techniques are being applied in textile coloration and related fields. Distribution of the reviewed articles was assessed in terms of the type of journals, year of publication, methods, and research background. Based on the citation network analysis method, an objective approach, CitNetExplorer and VOSviewer are used to identify the clusters. It is found that artificial intelligence techniques are mainly three‐layer artificial neural networks with different designs, which are mainly used in textile wastewater decolorisation fields, including wastewater treatment and colour fading, such as colour prediction and real‐time monitoring of textile wastewater, dye removal efficiency, and dye degradation. Finally, the future research direction and limitations of this article are put forward.

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network.

Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles’ structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials.

Fully automatic image colorization based on semantic segmentation technology.

Aiming at these problems of image colorization algorithms based on deep learning, such as color bleeding and insufficient color, this paper converts the study of image colorization to the optimization of image semantic segmentation, and proposes a fully automatic image colorization model based on semantic segmentation technology. Firstly, we use the encoder as the local feature extraction network and use VGG-16 as the global feature extraction network. These two parts do not interfere with each other, but they share the low-level feature. Then, the first fusion module is constructed to merge local features and global features, and the fusion results are input into semantic segmentation network and color prediction network respectively. Finally, the color prediction network obtains the semantic segmentation information of the image through the second fusion module, and predicts the chrominance of the image based on it. Through several sets of experiments, it is proved that the performance of our model becomes stronger and stronger under the nourishment of the data. Even in some complex scenes, our model can predict reasonable colors and color correctly, and the output effect is very real and natural.

Further insight into the global variability of the OCA2-HERC2 locus for human pigmentation from multiallelic markers

The OCA2-HERC2 locus is responsible for the greatest proportion of eye color variation in humans. Numerous studies extensively described both functional SNPs and associated patterns of variation over this region. The goal of our study is to examine how these haplotype structures and allelic associations vary when highly variable markers such as microsatellites are used. Eleven microsatellites spanning 357 Kb of OCA2-HERC2 genes are analyzed in 3029 individuals from worldwide populations. We found that several markers display large differences in allele frequency (10% to 35% difference) among Europeans, East Asians and Africans. In Europe, the alleles showing increased frequency can also discriminate individuals with (IrisPlex) predicted blue and brown eyes. Distinct haplotypes are identified around the variants C and T of the functional SNP rs12913832 (associated to blue eyes), with linkage disequilibrium r2 values significant up to 237 Kb. The haplotype carrying the allele rs12913832 C has high frequency (76%) in blue eye predicted individuals (30% in brown eye predicted individuals), while the haplotype associated to the allele rs12913832 T is restricted to brown eye predicted individuals. Finally, homozygosity values reach levels of 91% near rs12913832. Odds ratios show values of 4.2, 7.4 and 10.4 for four markers around rs12913832 and 7.1 for their core haplotype. Hence, this study provides an example on the informativeness of multiallelic markers that, despite their current limited potential contribution to forensic eye color prediction, supports the use of microsatellites for identifying causing variants showing similar genetic features and history.

Predicting eye and hair colour in a Norwegian population using Verogen’s ForenSeq™ DNA signature prep kit

Prediction of eye and hair colour from DNA can be an important investigative tool in forensic cases if conventional DNA profiling fails to match DNA from any known suspects or cannot obtain a hit in a DNA database. The HIrisPlex model for simultaneous eye and hair colour predictions was developed for forensic usage. To genotype a DNA sample, massively parallel sequencing (MPS) has brought new possibilities to the analysis of forensic DNA samples. As part of an in-house validation, this study presents the genotyping and predictive performance of the HIrisPlex SNPs in a Norwegian study population, using Verogen’s ForenSeq™ DNA Signature Prep Kit on the MiSeq FGx system and the HIrisPlex webtool. DNA-profiles were successfully typed with DNA input down to 125 pg. In samples with DNA input < 125 pg, false homozygotes were observed with as many as 92 reads. Prediction accuracies in terms of AUC were high for red (0.97) and black (0.93) hair colours, as well as blue (0.85) and brown (0.94) eye colours. The AUCs for blond (0.72) and brown (0.70) hair colour were considerably lower. None of the individuals was predicted to have intermediate eye colour. Therefore, the error rates of the overall eye colour predictions were 37% with no predictive probability threshold (pmax) and 26% with a probability threshold of 0.7. We also observed that more than half of the incorrect predictions were for individuals carrying the rs12913832 GG genotype. For hair colour, 65% of the individuals were correctly predicted when using the highest probability category approach. The main error was observed for individuals with brown hair colour that were predicted to have blond hair. Utilising the prediction guide approach increased the correct predictions to 75%. Assessment of phenotype-genotype associations of eye colours using a quantitative eye colour score (PIE-score), revealed that rs12913832 AA individuals of Norwegian descent had statistically significantly higher PIE-score (less brown eye colour) than individuals of non-northern European descent. To our knowledge, this has not been reported in other studies. Our study suggests that careful assessment of the target population prior to the implementation of forensic DNA phenotyping to case work is beneficial.

A deep learning approach to the forward prediction and inverse design of plasmonic metasurface structural color

This report details a deep learning approach to the forward and inverse designs of plasmonic metasurface structural color. Here, optimized Deep Neural Network models are presented to enable the forward and inverse mapping between metamaterial structure and corresponding color. The forward model is capable of predicting color with &amp;gt;96% accuracy, with a 105 order of magnitude decrease in computational time when compared to finite-difference time-domain simulations used in conventional design workflows. An inverse model is trained using a tandem autoencoder, employing the pre-trained forward model. Here, the use of synthetic training data for self-learning is reported, which results in an ≈15% improvement in training accuracy. The tightly constrained inverse model allows for the instantaneous design of metasurfaces, given a desired color, with an accuracy of &amp;gt;86%, making it suitable for commercial use as well as the acceleration of photonics research.

Searching for improvements in predicting human eye colour from DNA

Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

Relationship among egg quality traits in Japanese quails and prediction of egg weight and color using data mining algorithms.

The present paper aims to predict egg weight from some egg quality characteristics (egg weight, yolk weight, albumen weight, shell thickness, shell weight, shape index, albumen index, yolk index, yolk scale, and Haugh unit) in Japanese quails and to classify 94 eggs collected in regards to their shell colors. In the present study, CART and MARS data mining algorithms were assessed in the prediction of egg weight of the quails with the content of detecting egg quality standards of the studied quail genotypes for breeding and marketing strategies. In the classification of the collected quail eggs on their shell colors, classification performances of CART, CHAID, exhaustive CHAID, and QUEST algorithms were measured. Among those, CART was selected as the best classification algorithm according to eggshell color. The highest significant correlations were obtained for egg weight-yolk weight (0.740) and egg weight-albumen weight (0.735), respectively, in considering egg internal and external quality traits in quails. CART algorithm more accurately classified all eggshell colors compared with other algorithms. MARS showed much better predictive performance than CART that produced 0.850 Rsq and 0.728 cross-validation Rsq for prediction of egg weight in quails. In conclusion, the obtained results revealed that data mining algorithms may be useful references in practice for quail breeders in the development of new selection strategies and characterization of the studied animal materials.