Red Green Blue Model Research Articles

White analytical chemistry-driven stability-indicating concomitant chromatographic estimation of thiocolchicoside and aceclofenac using response surface analysis and red green blue model.

White analytical chemistry is a novel concept for the assessment of analytical methods on basis of its validation efficiency, greenness power and economical efficiency. White analytical chemistry-driven stability indicating chromatographic method has been developed for the concomitant analysis of thiocolchicoside and aceclofenac. The proposed chromatographic method has been developed using a safe and environmental-friendly organic solvent for the concomitant stability study of thiocolchicoside and aceclofenac. The analytical risk assessment was carried out for the identification of high-risk analytical risk factors and analytical method performance attributes. The mixture design was applied for the design of experiments-based response surface modelling of high-risk analytical risk factors and analytical method performance attributes. The degradation products were isolated and characterized using infra-red, nuclear magnetic resonance and mass spectral data. The proposed method was compared for its validation efficiency, greenness power and cost-efficiency with published chromatographic methods using the red, green and blue models. The white score of the proposed and reported method was calculated by averaging the red, blue and green scores of the methods. The proposed method was found to be robust, green and economical for the concomitant stability study of thiocolchicoside and aceclofenac. This article is protected by copyright. All rights reserved.

Developed and Validated Capillary Isotachophoresis Method for the Rapid Determining Organic Acids in Children's Saliva.

One of the current challenges facing researchers is the search for alternative biological material, as opposed to routinely and invasively collected (such as blood), as the analysis of the former would provide information about the state of human health, allowing for the diagnosis of diseases in their early stages. With the search for disease biomarkers in alternative materials, the development of newer analytical solutions has been observed. This study aims to develop a reliable analytical method using the capillary isotachophoresis technique for the determination of organic acids in children's saliva, the presence/elevation of which can be used in the future for diagnostic purposes. Organic acids such as formic, lactic, acetic, propionic, and butyric acid, were determined in the saliva of healthy children without carious lesions. The limit of quantification determined in the validation process was found to vary from 0.05 to 1.56 mg/L, the recoveries at the two levels were determined to vary between 90% and 110% for level I, while for level II the corresponding values of 75% and 106% were found; the presentation, expressed as relative standard deviation values (RSD), did not exceed 5%. The parameters determined while validating the results method indicated that the obtained are reliable. The Red-Green-Blue (RGB) additive color model was used for the evaluation of the method. This comparative analysis allowed us to define the color of the method, which expresses whether it meets the given assumptions and requirements. According to the RGB model, the isotachophoresis method developed requires less reagent input, shorter sample preparation times, and results with lower energy consumption. Thus, the subject procedure may provide an alternative, routine tool for determining organic acids in human saliva, to be applied in the diagnosing of diseases of various etiological origins.

CloudNet: A LiDAR-Based Face Anti-Spoofing Model That Is Robust Against Light Variation

Face anti-spoofing (FAS) is a technology that protects face recognition systems from presentation attacks. The current challenge faced by FAS studies is the difficulty in creating a generalized light variation model. This is because face data are sensitive to light domain. FAS models using only red green blue (RGB) images suffer from poor performance when the training and test datasets have different light variations. To overcome this problem, this study focuses on light detection and ranging (LiDAR) sensors. LiDAR is a time-of-flight depth sensor that is included in the latest mobile devices. It is negligibly affected by light and provides 3D coordinate and depth information of the target. Thus, a model that is resistant to light variations and exhibiting excellent performance can be created. For the experiment, datasets collected with a LiDAR camera are built and CloudNet architectures for RGB, point clouds, and depth are designed. Three protocols are used to confirm the performance of the model according to variations in the light domain. Experimental results indicate that for protocols 2 and 3, CloudNet error rates increase by 0.1340 and 0.1528, whereas the error rates of the RGB model increase by 0.3951 and 0.4111, respectively, as compared with protocol 1. These results demonstrate that the LiDAR-based FAS model with CloudNet has a more generalized performance compared with the RGB model.

Quantitative evaluation of radiodermatitis following whole-breast radiotherapy with various color space models: A feasibility study.

PurposeWe analyzed skin images with various color space models to objectively assess radiodermatitis severity in patients receiving whole-breast radiotherapy.MethodsTwenty female patients diagnosed with breast cancer were enrolled prospectively. All patients received whole-breast radiotherapy without boost irradiation. Skin images for both irradiated and unirradiated breasts were recorded in red-green-blue (RGB) color space using a mobile skin analysis device. For longitudinal analysis, the images were acquired before radiotherapy (RTbefore), approximately 7 days after the first fraction of radiotherapy (RT7days), RT14days, and approximately 10 days after radiotherapy completion (RTafter). Four color space models (RGB, hue-saturation-value (HSV), L*a*b*, and YCbCr models) were employed to calculate twelve color space parameters for each skin image. Skin dose measurements for irradiated breasts were performed using nanoDot optically stimulated luminescent dosimeters on the first fraction of radiotherapy. Subsequently, acute radiation dermatitis in each patient was assessed according to the Radiation Therapy Oncology Group scoring criteria at both RT14days and RTafter. Finally, several statistical analysis methods were applied to investigate the performance of the color space parameters to objectively assess the radiodermatitis.ResultsOwing to radiation-induced skin damage, R value of RGB model as well as S and V values of the HSV model for irradiated breasts increased significantly, while those for unirradiated breasts showed smaller increases. These parameters showed the longitudinal changes in color space parameters within each group and between groups over time with statistical significance. Strong correlations of the parameters for irradiated breasts at RT7days with skin doses and those at RTafter were observed with statistical significance.ConclusionThe R value of RGB model as well as the S and V values of HSV model showed relatively better performance in evaluating the acute radiation dermatitis. These color space parameters could therefore serve as useful tools to assess radiodermatitis severity in a dose-dependent manner.

HSV model based data hiding in video for watermark applications

This manuscript proposes a new data hiding approach that is used in watermark applications in video by transforming the RGB model to HSV model. This method initially estimates the number of frames needed to embed the data (watermark). Then two sets of RGB (Red, Green, Blue) coefficients (R1, G1, B1), (R2, G2, B2) are converted to HSV (Hue, saturation, values) Coefficients (H1, S1, V1) and (H2, S2, V2). The ‘Value’ Coefficients V1 and V2 are used to embed the watermark, since there exists a strong correlation between the adjacent ‘Value’ Coefficients. The same process is repeated on adjacent HSV coefficients till the watermark is fully embedded. After embedding the data HSV coefficients are again converted back to RGB coefficients. During the extraction phase, the data is extracted by transforming the RGB coefficient to HSV coefficients. One bit of information can be extracted from two adjacent HSV coefficients. Experimental outcomes show that the proposed watermarking approach is efficiently against attacks, viz noise, filtering, etc. Also, the proposed method performs better than traditional watermarking methods with the help of embedding rate (bpp), Structural similarity index measurement (SSIM), Visual quality (PSNR), Normalized cross-correlation (NC).

Assessing rice lodging using UAV visible and multispectral image

ABSTRACT Rice lodging may result in serious yield loss. Manual in-situ assessments of lodging are inefficient and inaccurate. Therefore, this paper explores the potential of unmanned aerial vehicle (UAV) image in evaluating rice lodging. Multispectral and red–green–blue (RGB) cameras mounted on UAV platforms were used to acquire images of two rice paddies in Shanghai, China. The image features of non-lodged and lodged rice, including their spectral reflectance, vegetation indices, texture, and colour, were extracted and analysed to optimize the indicators for lodging detection. Rice lodging detection models based on the selected image features were built to discriminate between non-lodged and lodged rice. Results revealed that the reflectances at the green and red-edge bands are the most common optimal indicators of lodged rice in the multispectral images. Moreover, the mean texture of the green channel and lightness value were determined to be the optimal indicators of rice lodging in the RGB image. The accuracies of the proposed lodging detection models exceeded 90%, and the multispectral lodging detection model was proved to be more accurate than the corresponding RGB model. Our results confirm that UAV-based remote sensing can play an important role in assessing rice lodging.

A visual approach for defining the spatial relationships among crashes, crimes, and alcohol retailers: Applying the color mixing theorem to define the colocation pattern of multiple variables

In traffic safety studies, the few scholars who have focused on analyzing disaggregated data obtained results that have been either difficult to explain or demonstrate because they did not provide clear visual maps or utilize statistical tests to quantify the spatial relationships. In order to increase the use of such disaggregated spatial methods for use in traffic safety studies, the current study documents the application of a new RGB (red, green, blue) model which combines the color additive theorem and the kernel density map (KDE) to define crash colocation patterns and the coincidence spaces of related variables.This study contributes to the literature in three major ways: (1) a new RGB model was established and applied in the field of traffic safety; (2) the variable dimensions were expanded from two to three; and, (3) the dimension of uncertainty was also included. When the new RGB model was utilized with data collected in College Station, Texas, the results indicated that the new colocation map is able to clearly and accurately define colocation hotspots of crashes, crimes, and alcohol retailers. As expected, these hotspots are located in areas with many bars, the largest strip malls and busiest intersections. The intensity maps have provided results consistent with the above colocation maps. However, the uncertainty map does not show a relatively higher level of certainty regarding the location of hotspots as we expected because the input of each variable was not related to the highest kernel value. Therefore, future scholars should focus on the colocation and intensity maps while using the uncertainty map as a reference for individual event risk evaluation only.

Dual upconversion nanophotoswitch for security encoding

Light-harvesting lanthanide ions (Ln3+) doped NaYF4 inks could provide polychromatic patterns for opposing counterfeiting commodity infestation because of their distinctive upconversion photoluminescence (UPL) properties. Herein, three kinds of core-triple-shell Ln3+ ions doped NaYF4 upconversion nanocrystals (UCNCs) are synthesized through modified high-temperature coprecipitation, which demonstrate excellent UPL properties of independent emitting colors under 808 or 980 nm laser excitation. Additive mixing three kinds of 808 nm emitted red-green-blue (RGB) UCNCs colloid solution can precisely regulate the emissions of the suspension for achieving full-color display. The as-obtained RGB three-primary colors induced by 808 nm laser accomplish broader color gamut than traditional standard RGB (sRGB) model and printing cyan-magenta-yellow (CMY) model. In addition, various China zodiac patterns and complex multicolor images are printed by the as-formulated UCNCs inks through screen printing technology. The printed patterns present colorful and polychromatic sequential toning visualization patterns under 808 nm excitation, while present another succession of gradually changed versatile patterns under 980 nm excitation. As a proof of concept, transparent polyvinyl chloride (PVC) self-adhesive anti-counterfeiting label is attached to the bottle of wine package for practical application. The demonstration of multiple model patterns of Chinese zodiac and poetry images based on these core-triple-shell UCNCs can be selected as a conceivable substitute of traditional single model patterns, underlining the full-color anti-counterfeiting level.

Protective effects of biodegradable collagen implants on thinned sclera after strabismus surgery: a paired-eye study

To determine the efficacy of a biodegradable Ologen (Aeon Astron Europe BV, Leiden, The Netherlands) collagen matrix in reducing the blue color change due to exposed thinned sclera after strabismus surgery. Fourteen patients with intermittent exotropia undergoing symmetric bilateral lateral rectus recession surgery were included in this prospective, randomized, paired-eye controlled study. In each patient, Ologen was implanted at the original rectus insertion site in one randomly selected eye; the other eye underwent conventional surgery. Ologen was inserted under the conjunctiva without suturing, covering the muscle insertion site. Conjunctival color change was analyzed using computer-based image analysis immediately and 1week, 1month, and 3months postoperatively. Slit-lamp photographs of each eye were evaluated using contrast limited adaptive histogram equalization (CLAHE), Canny edge, and the RGB (red-green-blue) model. Secondary outcomes were conjunctival and sclera thickness 3months postoperatively determined by anterior segment optical coherence tomography. Immediately and 1week postoperatively all color models showed no significant differences between Ologen-implanted and control eyes. Three months postoperatively, Ologen-implanted eyes exhibited significantly lower CLAHE (P=0.041) and RGB model blue color (P=0.008) values than control eyes. Canny edge (P=0.061) and RGB model red color (P=0.152) values did not differ between eyes. Conjunctival stroma and episcleral complex thickness was greater in Ologen-implanted eyes than in controls (P=0.001). Blue color change was significantly less noticeable in Ologen-implanted eyes than in controls. Thus, Ologen implantation helps prevent visible blue sclera at the original rectus insertion site after lateral rectus recession.

Estimating light interception using the color attributes of digital images of cotton canopies

Crop growth and yield depend on canopy light interception (LI). To identify a low-cost and relatively efficient index for measuring LI, several color attributes of red-green-blue (RGB), hue-saturation-intensity (HSI), hue-saturation-value (HSV) color models and the component values of color attributes in the RGB color model were investigated using digital images at six cotton plant population densities in 2012–2014. The results showed that the LI values followed downward quadratic curves after planting. The red (R), green (G) and blue (B) values varied greatly over the years, in accordance with Cai's research demonstrating that the RGB model is affected by outside light. Quadratic curves were fit to these color attributes at six plant population densities. Additionally, linear regressions of LI on every color attribute revealed that the hue (H) values in HSI and HSV were significantly linearly correlated with LI with a determination coefficient (R2)≥0.89 and a root mean square error (RMSE)=0.05. Thus, the H values in the HSI and HSV models could be used to measure LI, and this hypothesis was validated. The H values are new indexes for quantitatively estimating the LI of heterogeneous crop canopies, which will provide a theoretical basis for optimizing the crop canopy structure. However, further research should be conducted in other crops and under other growing and environmental conditions to verify this finding.

Study on Classification Corn Seedling and Weed Based on RGB Model and HSV Model

RGB (Red-Green-Blue) model and HSV (Hue-Saturation-Value) model are used to classify corn seeding and weed. Plants are distinguished from background based on color feature, and the binary image is acquired. As foreground in binary image, corn seedling and weed are labelled effectively with a set of numbers after clearing noise and labelling connected components. Then saturation energy of each connected component with a certain label can be calculated, and the maximum saturation energy is corresponded to the region of corn seedling. Furthermore, in hue image, corn seedling root has larger hue value, so the location of root is acquired. Therefore, we can not only classify corn seedling and weed, but also acquire location of corn seedling root, and the results show that the method of classification corn seedling and weed based on RGB model and HSV model has a great accuracy and real-time performance.

La scelta del colore in odontoiatria

ObjectivesA correct color choice may be performed either by the traditional method, relying on the color scale guide, or by using recent colorimetric technologies, digital spectrophotometry and the RGB (Red Green Blue) model. This study aims to compare the use of traditional color scales to spectrophotometers in order to evaluate their respective efficacy. Materials and methodsA literature review on color choice in restorative dentistry was carried out, using specific key words and selection criteria, and the obtained results were analyzed. Results and discussionIn the reviewed articles, the color scale guide still played a unique role as a visual reference, deemed useful, both at the beginning and the end of the treatment, for the final result control. ConclusionsWithin the scope of our literature review, the perception of color and the final quality assessment of a product rely, both for the operator and for the patient, on the visual evaluation method. Digital spectrophotometric technology may act as an aid to the correct color choice, although the visual assessment is still the gold standard.

Prediction of pork color attributes using computer vision system

Color image processing and regression methods were utilized to evaluate color score of pork center cut loin samples. One hundred loin samples of subjective color scores 1 to 5 (NPB, 2011; n=20 for each color score) were selected to determine correlation values between Minolta colorimeter measurements and image processing features. Eighteen image color features were extracted from three different RGB (red, green, blue) model, HSI (hue, saturation, intensity) and L*a*b* color spaces. When comparing Minolta colorimeter values with those obtained from image processing, correlations were significant (P<0.0001) for L* (0.91), a* (0.80), and b* (0.66). Two comparable regression models (linear and stepwise) were used to evaluate prediction results of pork color attributes. The proposed linear regression model had a coefficient of determination (R2) of 0.83 compared to the stepwise regression results (R2=0.70). These results indicate that computer vision methods have potential to be used as a tool in predicting pork color attributes.

Tongue color changes within a menstrual cycle in eumenorrheic women

Tongue color (舌色 shé sè) has been used to diagnose abnormal body conditions for thousands of years in traditional Chinese Medicine (中醫 zhōng yī). However, it is not clear whether tongue color alters with physiological changes within a normal menstrual cycle (月經周期 yuè jīng zhōu qī). This study investigated difference in tongue color between the follicular phase and luteal phase in eumenorrheic women. Tongue surface photographs were taken in the follicular phase and the luteal phase of thirty-two volunteers with biphasic basal body temperature. Color values on five areas of the tongue surface were examined and comparisons of color values were made between the two phases according to the red–green–blue (RGB), hue–saturation–brightness (HSB), luminance-a-b (Lab), and cyan–magenta–yellow–black (CMYK) models. Based on the RGB model, the values of green and blue in the tip area were larger in the follicular phase than both in the luteal phase. The values of magenta and yellow based in the CMYK model were smaller in the tip area in the follicular phase than that in the luteal phase. The saturation in the tip area was smaller in the follicular phase than that in the luteal phase. Based on the Lab model, b value in the middle area was smaller in the follicular phase than that in the luteal phase. The data revealed that tongue color varied within a eumenorrheic menstrual cycle, suggesting that tongue color differences between the follicular and luteal phases need to be considered while practicing tongue diagnosis (舌診 shé zhěn) or performing clinical studies among childbearing women.

New Vector Ordering in the RedGreenBlue Colour Model with Application to Morphological Image Magnification

In this paper we present a new vector ordering ⪯RGB for colours modelled in the RedGreenBlue colour model. The RedGreenBlue colour model becomes with this new ordering and associated minimum and maximum operators a complete lattice. We also have defined a complement co for colours in the RedGreenBlue model, with which our new ordering is compatible. As an application we illustrate the extension of our morphological magnification method, described in 8 and 1, towards colour images with sharp edges modelled in the RedGreenBlue model. There we have needed the compatibility of ⪯RGB with co to detect the corners in an image using the hit-or-miss transformation. Experimental results demonstrate that our method gives very good results.

A perceptually based spectral model for isotropic textures

Color is routinely used to visualize interval and ordinal data, while texture is not. For color, a variety of perceptually based models is available, which can be used to convey data via straightforward mapping. The dimensionality of texture is less well understood and there are almost no perceptually based and validated models available to generate textures on demand. We present a perceptually based texture synthesis model for isotropic textures. The model uses additive synthesis of band-limited noise in the spectral domain, comparable to the RGB (red, green, blue) model for color. Via user experiments, we have derived a three-dimensional model to control the amplitudes per band in a perceptually intuitive way, comparable to the HSV (hue, saturation, value) model for color. The three dimensions used are contrast, spatial frequency, and spectral purity. Besides a mapping to the amplitudes, we have derived a mapping to a perceptually equidistant space. We show how such textures can be combined with color; applications are presented.

Measurement of the geometrical features and surface color of rapeseeds using digital image analysis

Digital image analysis was applied to determine the geometrical features and color of rape seeds surface, and to discriminate some impurities, that are difficult to separate in the cleaning process. The paper notices on methodological aspects, and the experiment described constitutes the first stage of studies on the possibility of applying digital image analysis to rapeseed quality estimation, so the results obtained should be treated as preliminary. The geometrical features of seeds and their color were analyzed using the LUCIA G ver. 4.8 software. It was found that variation in geometrical dimensions of seeds was much lower than in color of their surface, so minimum sample size utilized for color measurements should be larger. The surface color of seeds was feature that insufficiently differentiates seeds of different dimensions. Only small seeds were characterized by somewhat changed distribution of color on their surface. An analysis of color of rape and stickywilly seeds in RGB (red/green/blue) model showed distinct differences in value ranges, enabling to distinguish between these seeds. Surface color of mature, immature and broken seeds cannot be used to distinguish these fractions.

Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy

Transmitted light microscopy is used in pathology to examine stained tissues. Digital image analysis is gaining importance as a means to quantify alterations in tissues. A prerequisite for accurate and reproducible quantification is the possibility to recognise stains in a standardised manner, independently of variations in the staining density. The usefulness of three colour models was studied using data from computer simulations and experimental data from an immuno-doublestained tissue section. Direct use of the three intensities obtained by a colour camera results in the red-green-blue (RGB) model. By decoupling the intensity from the RGB data, the hue-saturation-intensity (HSI) model is obtained. However, the major part of the variation in perceived intensities in transmitted light microscopy is caused by variations in staining density. Therefore, the hue-saturation-density (HSD) transform was defined as the RGB to HSI transform, applied to optical density values rather than intensities for the individual RGB channels. In the RGB model, the mixture of chromatic and intensity information hampers standardisation of stain recognition. In the HSI model, mixtures of stains that could be distinguished from other stains in the RGB model could not be separated. The HSD model enabled all possible distinctions in a two-dimensional, standardised data space. In the RGB model, standardised recognition is only possible by using complex and time-consuming algorithms. The HSI model is not suitable for stain recognition in transmitted light microscopy. The newly derived HSD model was found superior to the existing models for this purpose.