CIELAB Color Space Research Articles

Colour-stability analysis for estimation of deterioration in concrete due to chemical attack

Inaccessibility and subjectivity in damage evaluation during visual inspection may often lead to faulty assessment of concrete. Digital imaging tools can be used to identify and quantify the extent of distress due to deteriorating agents on concrete. Structures subjected to environmental degradation manifest deterioration in terms of changes in surface characteristics. It is strongly believed that there lies a co-dependency between the extent of deterioration and the reduction in the mechanical and durability performance. In the present study, digital image analysis technique, namely Colour-Stability Analysis (CSA), has been performed on cube samples of 30 MPa grade concrete, immersed in acid solutions (HCl, H2SO4), chloride (NaCl) and sulphate (MgSO4) at 5% concentration, exposed for various periods of 3, 7, 14 and 28 days, after the end of the requisite 28-day curing period. The CSA is conducted in CIE-LAB colour space as it is device-independent and represents the human perception of colour. The colour profiles of the deteriorated surfaces before and after immersion in the above reagents for 3, 7, 14 and 28 days have been quantified. The successive and relative differences in the surface colour profiles for respective reagent-immersion and exposure conditions have been computed and analysed. The current study revealed the influence and mechanism of various reagent’s impact on the deterioration of concrete surface with time and the same is quantified. The correlation between the extent of damage in concrete in terms of change in colour profiles, residual strength and durability is established.

Effects of Transition Metal Ions on the Colour of Blue-Green Beryl

This study reports the effects of transition metal ions on the colour of blue-green beryl. Industrial cameras were used to measure colour in the CIELAB colour space. X-ray fluorescence (XRF), X-ray diffraction (XRD), infrared spectroscopy (IR), and ultraviolet-visible (UV–vis) spectroscopy were used for characterization. The d–d transition of Fe3+ with sixfold coordination, the O2−→Fe3+ charge transfer, and the charge transition of binuclear metal M–M complexes formed by [Fe2(OH)4]2+ in the channel caused a yellow tone, whereas the charge transfer of Fe2+/Fe3+ with sixfold coordination caused a blue-green tone. The chroma of blue-green beryl was negatively correlated with the ratio of Cs+Mn to Fe contents. The lightness of blue-green beryl was negatively correlated with the total content of transition metal ions.

Development of a Screening Method for Sulfamethoxazole in Environmental Water by Digital Colorimetry Using a Mobile Device

Antibiotic resistance is a major health concern of the 21st century. The misuse of antibiotics over the years has led to their increasing presence in the environment, particularly in water resources, which can exacerbate the transmission of resistance genes and facilitate the emergence of resistant microorganisms. The objective of the present work is to develop a chemosensor for screening of sulfonamides in environmental waters, targeting sulfamethoxazole as the model analyte. The methodology was based on the retention of sulfamethoxazole in disks containing polystyrene divinylbenzene sulfonated sorbent particles and reaction with p-dimethylaminocinnamaldehyde, followed by colorimetric detection using a computer-vision algorithm. Several color spaces (RGB, HSV and CIELAB) were evaluated, with the coordinate a_star, from the CIELAB color space, providing the highest sensitivity. Moreover, in order to avoid possible errors due to variations in illumination, a color palette is included in the picture of the analytical disk, and a correction using the a_star value from one of the color patches is proposed. The methodology presented recoveries of 82–101% at 0.1 µg and 0.5 µg of sulfamethoxazole (25 mL), providing a detection limit of 0.08 µg and a quantification limit of 0.26 µg. As a proof of concept, application to in-field analysis was successfully implemented.

Plant leaf images computerized segmenation

In this paper the comparison of RGB, HSV and CIELab color spaces is considered in view of diseased leaf images segmentation by color thresholding method. In such tasks HSV and CIELab outperform RGB. Thresholding method based upon HSV or CIELab color spaces can be applied to measuring leaves total area, diseased and healthy surfaces area, as well as dataset composing in machine learning.

Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement.

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size 1024×1024×3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj_MMLE.html.

Investigating the Possibility of Determining the Sequence Intersecting Lines between LaserJet Printing and Handwriting in Document Examination using Color Measurement Technique

In Questioned Documents Examination, the sequence of crossing lines in the intersection of handwriting and printed area can be important clues for detecting tampered documents. Recognition of such documents is a arduous task and requires people with experience and expertise. In the present work, we investigated the possibility of determining the sequence of intersecting lines between LaserJet printing and handwriting for a series of simulated laboratory specimens in the document examination using color measurement technique. The spectral reflectance curves and color coordinates of some points on and out of the cross lines were compared. Four different commercial ballpoint pens and a black toner LaserJet were used to prepare the specimens. The color change of the intersecting lines was subjectively considered through the captured images and a visual assessment process. It was also objectively determined by determining the color difference values from the colorimetric data in CIELAB and CIELCH color spaces in the visible range. The color change evaluation showed that the order in which printing or handwriting is applied alters colorimetric results. Moreover, the investigations showed small color difference values of less than 2 units between a point of printed area individually, and intersection could be applied as a tolerance limit for pass/fail judgments.

Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough?

Histopathologic evaluation of Hematoxylin & Eosin (H&E) stained slides is essential for disease diagnosis, revealing tissue morphology, structure, and cellular composition. Variations in staining protocols and equipment result in images with color nonconformity. Although pathologists compensate for color variations, these disparities introduce inaccuracies in computational whole slide image (WSI) analysis, accentuating data domain shift and degrading generalization. Current state-of-the-art normalization methods employ a single WSI as reference, but selecting a single WSI representative of a complete WSI-cohort is infeasible, inadvertently introducing normalization bias. We seek the optimal number of slides to construct a more representative reference based on composite/aggregate of multiple H&E density histograms and stain-vectors, obtained from a randomly selected WSI population (WSI-Cohort-Subset). We utilized 1,864 IvyGAP WSIs as a WSI-cohort, and built 200 WSI-Cohort-Subsets varying in size (from 1 to 200 WSI-pairs) using randomly selected WSIs. The WSI-pairs' mean Wasserstein Distances and WSI-Cohort-Subsets' standard deviations were calculated. The Pareto Principle defined the optimal WSI-Cohort-Subset size. The WSI-cohort underwent structure-preserving color normalization using the optimal WSI-Cohort-Subset histogram and stain-vector aggregates. Numerous normalization permutations support WSI-Cohort-Subset aggregates as representative of a WSI-cohort through WSI-cohort CIELAB color space swift convergence, as a result of the law of large numbers and shown as a power law distribution. We show normalization at the optimal (Pareto Principle) WSI-Cohort-Subset size and corresponding CIELAB convergence: a) Quantitatively, using 500 WSI-cohorts; b) Quantitatively, using 8,100 WSI-regions; c) Qualitatively, using 30 cellular tumor normalization permutations. Aggregate-based stain normalization may contribute in increasing computational pathology robustness, reproducibility, and integrity.

Leaf Blights Detection and Classification in Large Scale Applications

Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of plants by themselves when recognizing diseases. In this article, a variety of machine learning methods are used to classify and detect leaf blight. We use the fusion of deep convolutional neural network (CNN) models obtained from SqueezeNet and ShuffleNet to improve the accuracy and robustness of large-scale applications. We use entropy to reduce the complexity of the calculation process and reduce the features in the deep learning process. In addition, we use a support vector machine (SVM) classifier to obtain the classification. We use the CIELAB color space to capture the entire color range to improve accuracy. Our results are very promising because we have achieved 98% accuracy in the early detection of leaf blight.

Assessing grass water use efficiency through smartphone imaging and ImageJ analysis

Overwatering and underwatering grass are widespread issues with environmental and financial consequences. Current approaches to assessing grass water use efficiency (WUE) are inaccessible to the public. We developed an accessible method to assess grass WUE: combining smartphone imaging with open access color unmixing analysis. Images were converted from the RGB color space to the CIELAB color space using ImageJ—an open access, user-friendly software—to correct for uneven lighting without compromising image detail. We obtained parameters a* (unmixes green-to-red vector) and b* (unmixes blue-to-yellow vector). We hypothesized that WUE can be accurately determined from grass color and growth, which can be analyzed using CIELAB color unmixing. We tested how nine watering levels (100–900 mL every 4–5 days) affected Stenotaphrum secundatum (St. Augustine grass) over one month in Orange County, Southern California. Color was quantified using a*:b* ratios, growth was tracked using grass area, and pigment composition was analyzed using plot profiles. We analyzed whole samples in the uncontrolled real-world environment, individual leaves in a controlled homemade imaging box, and extracted pigments before and after paper chromatography. Results were clustered using Gaussian finite mixture models, implemented by R package ‘mclust’. Overall trends for grass coverage, a*:b* ratios, and pigment composition were consistent with real-world observations. Cluster analyses were consistent across image types, identified an ideal watering range (600–700 mL), and differentiated between underwatered and overwatered grass. Our hypothesis was supported. Our method can be applied in automated irrigation systems or apps, providing grass WUE assessment for regular consumer use.

Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam

Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam. Ash wood samples of 20x20x30 mm were subjected to thermal modification in different conditions. The thermal modification was conducted in air at 190 °C and in steam at 160 °C. For both environments modification lasted 2, 6 and 10 hours. Samples color parameters were measured before and after thermal modification on the basis of the mathematical CIELab color space model. Changes in all parameters (L, a and b) were observed, the highest in lightness (L) - darker color. The total color difference (ΔE) and chromaticity change (ΔC) were calculated for all samples. The highest value of ΔE was obtained for wood modified in the air at 190 °C for 10 h. The highest value of ΔC was obtained for wood modified in steam at 160 °C for 10 h. However, the value obtained for wood modified in the air at 190 °C for 10 h were only slightly lower.

Effect of Issatchenkia terricola WJL-G4 on Deacidification Characteristics and Antioxidant Activities of Red Raspberry Wine Processing.

Our previous study isolated a novel Issatchenkia terricola WJL-G4, which exhibited a potent capability of reducing citric acid. In the current study, I. terricola WJL-G4 was applied to decrease the content of citric acid in red raspberry juice, followed by the red raspberry wine preparation by Saccharomyces cerevisiae fermentation, aiming to investigate the influence of I. terricola WJL-G4 on the physicochemical properties, organic acids, phenolic compounds and antioxidant activities during red raspberry wine processing. The results showed that after being treated with I. terricola WJL-G4, the citric acid contents in red raspberry juice decreased from 19.14 ± 0.09 to 6.62 ± 0.14 g/L, which was further declined to 5.59 ± 0.22 g/L after S. cerevisiae fermentation. Parameters related to CIELab color space, including L*, a*, b*, h°, and ∆E* exhibited the highest levels in samples after I. terricola WJL-G4 fermentation. Compared to the red raspberry wine pretreated without deacidification (RJO-SC), wine pretreated by I. terricola WJL-G4 (RJIT-SC) exhibited significantly decreased contents of gallic acid, cryptochlorogenic acid, and arbutin, while significantly increased contents of caffeic acid, sinapic acid, raspberry ketone, quercitrin, quercetin, baicalein, and rutin. Furthermore, the antioxidant activities including DPPH· and ABTS+· radical scavenging were enhanced in RJIT-SC group as compared to RJO-SC. This work revealed that I. terricola WJL-G4 had a great potential in red raspberry wine fermentation.

Skin color measurements before and after two weeks of sun exposure

We performed spectrophotometric measurements of skin reflectance at four body locations (forehead, cheek, neck, and back of hand), before and after two weeks of sun exposure, for 103 first-year college students. Skin reflectance was measured twice at each body location, before and after two weeks of sun exposure, obtaining an average repeatability (mean color difference from the mean) in the range of 0.2–0.5 CIELAB units (D65 illuminant, CIE 1931 standard observer). However, the average skin color differences before and after two weeks of sun exposure were in the range of 3.6–3.9 CIELAB units, considerably higher than measured repeatability, as a consequence of suntanning. Skin color appearance variation was analyzed in the CIELAB color space, and it was found that at all body locations two weeks of sun exposure made lightness L∗ and hue-angle hab significantly decrease, a∗ and chroma Cab∗ significantly increase, and b∗ shows no statistically significant changes (except for hab at the forehead and cheek, and for a∗ at the forehead where no statistically significant changes were found). An W shape for skin spectral reflectance between 520 nm and 600 nm was found at some of the four measured body locations. It was found that the individual typological angle (ITA) defined from L∗ and b∗ performed well in predicting our measured data and a modification of ITA using L∗ and Cab∗ performed even better, with the measured L∗ as reference. The color shifts produced by two weeks of sun exposure in different planes of CIELAB were analyzed for the skin categories established by the ITA index, and compared with the control group data accumulated by Amano et al. (PLoS ONE. 15(12), e0233816)(PLoS ONE 15(2020) e0233816). The measured skin spectra can be useful to the skin color database currently being developed by CIE TC 1-92.

Multi-scale residual attention network for single image dehazing

Single image dehazing is a fundamental but difficult task in image processing. Although various deep learning-based methods have achieved good dehazing performance, it is still a challenge to recover a haze-free image without color distortion from a hazy image. The color performance of nowadays dehazing methods needs further improvement. In this paper, the effect of haze on the luminance and chrominance of CIELAB color space is firstly studied. Based on CIELAB color space, a novel multi-scale residual attention network (MSRA-Net) is proposed for single image dehazing. The proposed network consists of two subnetworks: one is designed to remove haze from the luminance channel; another focuses on enhancing the chrominance components and makes the color more realistic. To improve the performance of MSRA-Net, the multi-scale residual attention block (MSRA-block) and feature aggregation building block (FAB-block) are presented. Both the subjective and objective performance show that the proposed method outperforms the existing dehazing algorithms, especially in terms of color performance.

Effects of Acidic Beverages on Color Stability of Bulk-Fill Composites with Different Viscosities

The aim of this in vitro study is to investigate the effects of acidic beverages on the discoloration of bulk-fill composites with different viscosities. 144 disc-shaped specimens (8mm diameter × 2mm thick) were prepared from four different composite resins including a nanohybrid (Filtek Ultimate), two packable bulk-fill (Filtek Bulk Fill, Tetric N Ceram Bulk Fill), and a flowable bulk-fill composite (Tetric N Flow Bulk Fill). The specimens of each group were divided into 3 subgroups and submerged in distilled water, orange juice and coke (n=12). Color difference (∆E) were measured using CIELab color space and a compact spectrophotometer (VITA Easyshade) at baseline and after 30 days of immersion. Data were analyzed using two-way ANOVA and Tukey's HSD post-hoc tests (P<0.05). Two-way ANOVA revealed that both material type and staining solution significantly influenced ∆E values (P<0.001). Immersion in orange juice resulted in greater and significant discoloration over time in comparison to distilled water and coke (P<0.05). Tetric N Ceram (∆E=7.0) and Tetric N Flow (∆E=7.7) bulk-fill restoratives showed highest color change values after 30 days of immersion in orange juice. This study suggests that a discoloration above the clinically acceptable threshold (∆E>2.7) was observed for all bulk-fill composites tested. It can be speculated that orange juice has a more negative effect on the color stability compared to coke. Additionally, flowable bulk-fill composite was more prone to discoloration than condensable bulk-fill composites.

No-reference stereoscopic image quality assessment on both complex contourlet and spatial domain via Kernel ELM

Stereoscopic imaging is widely used in many fields. To guarantee the best quality of experience, it is necessary to design a robust and accurate quality assessment model for stereoscopic content. In this paper, we proposed a no-reference stereoscopic image quality assessment (NR-SIQA) model using both complex contourlet and spatial domain features of monocular and binocular images. Monocular features extracted from the CIELAB color space are exploited to characterize quality degradation, including the across-scale and across-orientation correlation in complex contourlet domain and natural scene statistics-based (NSS) features in spatial domain. Then, the binocular features consist of energy along with energy difference and structural correlation in complex contourlet domain, and statistics distribution in spatial domain extracted from the synthesized cyclopean image and the 3D visual discomfort measure based on the statistics of disparity map. Finally, the above features are mapped into the predicted quality scores by the Kernel ELM (KELM) regression model. Experimental results on four public datasets show that the proposed model is highly consistent with human subjective perception in terms of accuracy and generalization.

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis

Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) have been made, to date, no viable method for radiation-induced skin reaction (RISR) analysis and classification is available. The objective of this single-center study was to develop machine learning and deep learning approaches using deep convolutional neural networks (CNNs) for automatic classification of RISRs according to the Common Terminology Criteria for Adverse Events (CTCAE) grading system. ScarletredⓇ Vision, a novel and state-of-the-art digital skin imaging method capable of remote monitoring and objective assessment of acute RISRs was used to convert 2D digital skin images using the CIELAB color space and conduct SEV* measurements. A set of different machine learning and deep convolutional neural network-based algorithms has been explored for the automatic classification of RISRs. A total of 2263 distinct images from 209 patients were analyzed for training and testing the machine learning and CNN algorithms. For a 2-class problem of healthy skin (grade 0) versus erythema (grade ≥ 1), all machine learning models produced an accuracy of above 70%, and the sensitivity and specificity of erythema recognition were 67–72% and 72–83%, respectively. The CNN produced a test accuracy of 74%, sensitivity of 66%, and specificity of 83% for predicting healthy and erythema cases. For the severity grade prediction of a 3-class problem (grade 0 versus 1 versus 2), the overall test accuracy was 60–67%, and the sensitivities were 56–82%, 35–59%, and 65–72%, respectively. For estimating the severity grade of each class, the CNN obtained an accuracy of 73%, 66%, and 82%, respectively. Ensemble learning combines several individual predictions to obtain a better generalization performance. Furthermore, we exploited ensemble learning by deploying a CNN model as a meta-learner. The ensemble CNN based on bagging and majority voting shows an accuracy, sensitivity and specificity of 87%, 90%, and 82% for a 2-class problem, respectively. For a 3-class problem, the ensemble CNN shows an overall accuracy of 66%, while for each grade (0, 1, and 2) accuracies were 76%, 69%, and 87%, sensitivities were 70%, 57%, and 71%, and specificities were 78%, 75%, and 95%, respectively. This study is the first to focus on erythema in radiation-dermatitis and produces benchmark results using machine learning models. The outcome of this study validates that the proposed system can act as a pre-screening and decision support tool for oncologists or patients to provide fast, reliable, and efficient assessment of erythema grading.

Influence of Firing Temperature on Phase Composition and Color Properties of Ceramic Tile Bodies.

This study is focused on the behavior of the cream-firing clays from Opoczno region (Poland). The ceramic masses on which tests were carried out consisted of cream-firingBorkowice clay and dolomite in two different grain sizes as an additive that changes the color of ceramic materials. Test samples were prepared by plastic method and fired at range of 1100–1240 °C. Phase composition of theinvestigated materials was characterized by XRD method with quantitive analysis of the amorphous phase determined by the Retvield technique. Color properties of the surface of the obtained ceramic materials were determined in CIE-Lab color space. The phase composition of the obtained ceramics depends on the firing temperature. The color of the surface of the ceramic materials also depends on the firing temperature. There was a tendency to decrease the brightness, decrease the blue shade, and increase the yellow shade of the surface of materials with increasing the temperature. The conducted tests allowed to conclude that the color of ceramic materials depends on their phase composition. The most important role in the formation of color correspond to the amorphous phase, formed during the process. The lower content of the amorphous phase in the material allows to obtain brighter products with a lower proportion of yellow, and therefore closer to white. Moreover, following tests were carried out: total water absorption, total open porosity, linear shrinkage, and flexural strength. With increasing the temperature, total water absorption and total open porosity decrease, and total linear shrinkage increases due to the progressive sintering process. Flexural strength increases with the increase of the firing temperature for materials consisting of Borkowice clay. The addition of dolomite introduced new pores into the material, which resulted in an increase in flexural strength at lower firing temperatures and a decrease in flexural strength at higher firing temperatures.

Detection of binocular chromatic fusion limit for opposite colors

When the input colors of the left and right eyes are different from one another, binocular rivalry may occur. According to Hering theory, opponent colors would have the most significant tendency for rivalry. However, binocular color fusion still occurs under the condition that each eye's opponent chromatic responses do not exceed a specific chromatic fusion limit (CFL). This paper detects the binocular chromatic fusion limit for opposite colors within a conventional 3D display color gamut. We conducted a psychophysical experiment to quantitatively measure the binocular chromatic fusion limit on four opposite color directions in the CIELAB color space. Due to color inconsistency between eyes may affect the binocular color fusion, the experiment was divided into two sessions by swapping stimulation colors of left and right eyes. There were 5 subjects and they each experienced 320 trials. By analyzing the results, we used ellipses to quantify the chromatic fusion limits for opposing colors. The average semi-major axis of the ellipses is 27.55 Δ E a b∗, and the average semi-minor axis is 16.98 Δ E a b∗. We observed that the chromatic fusion limit varies with the opposite color direction: the CFL on RedBlue-GreenYellow direction is greater than that on Red-Green direction, the latter being greater than that on Yellow-Blue direction and the CFL on RedYellow-GreenBlue direction is smallest. Furthermore, we suggested that the chromatic fusion limit is independent of the distribution of cells, and there is no significant change in the fusion ellipse boundaries after swapping left and right eye colors.

Vibrations Analysis of the Fruit-Pedicel System of Coffea arabica var. Castillo Using Time–Frequency and Wavelets Techniques

Colombian coffee production is well-known, and selective manual harvesting plays a vital task in guaranteeing high ripe coffee fruit rates in this process, leading to its known worldwide aroma and flavor. To maintain this quality approach, selective harvesting methods based on mechanical vibrations are a promising alternative for developing technologies that could accomplish the challenging Colombian coffee production context. In this study, a vibrations analysis in coffee fruits at three ripening stages was carried out to evaluate the dynamic behavior at two frequency windows: 10 to 100 Hz and 100 to 1000 Hz. Two groups of fruits previously classified in the CIELab color space were chosen for the vibration test study samples. Time and frequency signals were characterized via FFT (fast Fourier transform), and bump wavelets were determined to obtain the frequency–time magnitude scalograms. The measurements were obtained in three degrees of freedom over the fruits: one for measuring the input force (computed in voltage way) and the other two measured by the velocity. The results revealed frequency ranges with specific resonant peaks between 24 and 45 Hz, and close to 700 Hz, where the ripe fruits presented higher magnitudes in the calculated parameters. FFT of the velocity and scaled mechanical impedance were used to estimate these frequency ranges. This work is an important step to identify a “vibrational fingerprint” of each Coffea arabica var. Castillo fruit-ripening stage. However, we consider that more experiments should be performed to reconstruct the modal shape in each resonance. In future studies, fatigue analysis could show which are the most effective frequency ranges to detach the ripe fruits from the perspective of a real selective coffee-harvesting scenario.

Evaluation and consistency calibration of hyperspectral imaging system based on liquid crystal tunable filter for fabric color measurement

AbstractThe Hyperspectral Imaging System can obtain the spectral response value of each pixel in a fabric sample image while being applied to fabric color measurement. Compared with the common grating Hyperspectral Imaging System, the Hyperspectral Imaging System based on Liquid Crystal Tunable Filter (LCTF) has the advantages of small size, high spatial resolution, and easy integration, which has potential application prospects in textile industry. In this article, the performance of the LCTF Hyperspectral Imaging System for fabric color measurement was evaluated, and an improved model for calibrating the consistency between it and Datacolor 650 was proposed. First, the 210 Pantone cotton color cards were used as samples to study the color repeatability and reproducibility of the LCTF Hyperspectral Imaging System. For evaluation its color measurement ability, the measured reflectance values were converted to the CIELAB color space and compared with those of Datacolor 650. Then, a Radial Basis Function neural network model was proposed to calibrate the color consistency between LCTF Hyperspectral Imaging System and Datacolor 650 which can fully use the reflectance information of the whole band instead of only adjacent wavelengths. Experimental results show that the LCTF Hyperspectral Imaging System can accurately measure the fabric color with good repeatability and reproducibility, and the consistency between the two instruments was significantly improved based on the Radial Basis Function neural network model compared with the R model.