Color Model Research Articles

Utility and greenness appraisal of nuclear magnetic resonance for sustainable simultaneous determination of three 1,4-benzodiazepines and their main impurity 2-amino-5-chlorobenzophenone

A robust, stability-indicating, and eco-friendly proton nuclear magnetic resonance (1H-qNMR) method was developed for the concurrent determination of three 1,4-benzodiazepines (BDZs), namely diazepam (DZP), alprazolam (ALP), and chlordiazepoxide (CDP) and their common impurity, synthesis precursor, and degradation product; 2-amino-5-chlorobenzophenone (ACB). In the present method, a novel approach was developed for composing a green and cost-efficient solvent system as an alternative to the common NMR organic solvents utilizing 0.3 M sodium dodecyl sulfate prepared in deuterated water. The conducted method is characterized by simplicity with no need for sample pretreatment or labeling. Phloroglucinol was used as an internal standard. The chosen signals for the determinations of ALP, CDP, DZP and ACB were at 2.35 ppm (singlet), 2.84 ppm (singlet), 3.11 ppm (singlet), and 6.90 ppm (doublet of doublet), respectively. The proposed method possessed linearity over the concentration range of 0.25–15.0 mg ml−1 for DZP, ALP, CDP and of 0.5–25.0 mg ml−1 for ACB with LOD values of 0.06, 0.03, 0.07 and 0.16 mg ml−1 respectively, and LOQ values of 0.18, 0.09, 0.21 and 0.49 mg ml−1, respectively. Accuracy of the method was evidenced by excellent recovery% (99.57–99.90%) and small standard deviation (≥ 1.10) for the three analyzed drugs. Intra- and inter-day precision were determined with coefficient of variation ranging from 0.12 to 1.14 and from 0.72 to 1.67, respectively. For the studied compounds, appraisal of the method greenness was achieved via four approaches: Analytical Eco-Scale, Green Analytical Procedure Index (GAPI), Analytical greenness metric (AGREE), and RGB Additive Color Model. The results proved that the proposed method has the privilege of being a green analytical method.

Fire identification based on improved multi feature fusion of YCbCr and regional growth

Fire is one of the mutable hazards that damage properties and destroy forests. However, fire-like objects easily influence many existing image-based fire detection methods. In order to improve the performance of fire identification, this paper proposes a new fire identification algorithm by merging fire segmentation and multi feature fusion of fire. First, the improved YCbCr models in the reflection and non-reflection environment are constructed according to the color model. Simultaneously, the reflection and non-reflection conditions can be judged according to the segmented area. Second, the seed points are determined according to the weighted average of centroid of each connected region. Simultaneously, the fine segmentation of fire image is implemented according to the entropy of average contrast and uniformity within the connected region. Experiments show that the segmentation is not affected by image noises. Finally, the quantitative indicators of fire identification are given according to the coefficient of variation of area, the dispersion of centroid and the circularity. Cases show that the proposed identification method of fire not only accurately identifies fire, but also has a lower computation complexity than the deep learning method.

Decorrelation stretch for enhancing colour fundus photographs affected by cataracts

ABSTRACT A method of enhancing colour fundus photographs is proposed to reduce the effect of cataracts. The enhancement method employs a decorrelation stretch (DS) technique in an LCC colour model. The initial designed technique embeds Hubbard’s colouration model into DS parameters to produce enhanced results in a standard form of age-related macular degeneration (AMD) reading centres. The colouration model could modify to enhance the colour of lesions observed in diabetic retinopathy (DR). The proposed algorithm could improve the effect of cataracts on fundus images and provided good results when the density of the cataract was less than grade 2. In the case of images taken through cataracts higher than or equal to grade 2, some output results could become unusable when the cataract was in line with the macula.

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

AbstractIn the field of histopathology, it has been observed that most images are of poor quality as a result of inadequate luminance and contrast. The first step in identifying and analyzing them is to enhance their quality while maintaining their inherent characteristics. This paper presents a novel method for enhancing histopathology images' important features, brightness preservation, and contrast by utilizing the adaptive intensity transformation gamma function in the wavelet domain. The RGB histopathological color image is first converted to the HSV color model. The V component is then stretched in order to compensate for any color distortion. In the proposed scheme, the stretched V component is split into four sub‐bands using discrete wavelet transform (DWT). Next, it determines the optimal value of the gamma correction function in the DWT lower sub‐band. A correction factor is applied using the singular value decomposition (SVD) technique to determine the optimal gamma function value using the nature‐inspired particle swarm optimization method (NIPSO). The corrected LL sub‐band is then applied to the inverse DWT (IDWT) together with the other unprocessed sub‐bands. In addition, the contrast of each feature is enhanced by employing an adaptive histogram equalization model that is contrast‐limited. The proposed method was evaluated through experiments and validations on a variety of histopathology images retrieved from multiple databases. Several quantitative studies have demonstrated that the proposed method outperforms existing enhancement techniques in terms of entropy, edge preservation index, contrast ratio, and Universal Image Quality Index (UIQI). This proposed technique increases contrast, while preserving the inherent characteristics of the original image, resulting in better quality histopathology images that can be used in disease diagnosis and inspection.

Detecting Ocular Tb With Modified Co-Occurrence Matrix

Image mapping is a method both for identifying and retrieving similar images from a large database of digital images. This system utilizes the low level image features such as color, texture and shape for retrieving similar images. Among the low level image features, color and texture are perceptually important low level features. Hence this paper entitled “Detecting ocular TB with modified co-occurrence matrix ” uses the color and texture information for identifying similar images from the database. The texture features are extracted from images using the modified co-occurrence matrix which is called texton co-occurrence matrix and the RGB color model is used for color texture.

Sensing the Positions of Multi Droplets on a Piezoelectric Substrate Using a Smartphone Based on CIELab Color Model

A method for simultaneously sensing positions of multi droplets on a piezoelectric substrate was presented. A double D-value algorithm was presented to distinguish target droplets using CIELab color model. An application soft (app) was developed to sense positions of target droplets. In order to verify the presented method, black ink solution droplets on a 128°-YX LiNbO3 substrate were used to demonstrate for sensing droplets position. Results show that different number of target droplets can be distinguished and their positions can be simultaneously sensed. As an application, a 5 micro-liters black ink solution droplet was driven on the piezoelectric substrate by surface acoustic wave. Its position was sensed and its movement path was analyzed using developed app. In the meantime, the transporting velocity of the droplet was obtained, of which value is 0.33 mm/s from 6th second to 7th second at 27.5 dBm electric signal power.

Substances with a Single Visible Absorption Band Cannot Be Green.

Some chemistry textbooks use an inappropriate colour model when discussing the colour of a substance that absorbs light at a specific wavelength. More specifically, these textbooks state that substances that absorb only in the violet end of the visible spectrum are green-yellow and substances that absorb only in the red end are blue-green. This paper presents a more accurate approach.

Associations between the Avatar Characteristics and Psychometric Test Results of VK Social Media Users

Online social media has an increasing influence on people’s lives, providing tools for communication and self–representation. People’s digital traces are gaining attention as a reflection of their personality traits, enhancing the personality computing tasks in various areas. This study aims at the identification of statistical associations between psychometric scores from three questionnaires—the Big Five Inventory, Plutchik’s Lifestyle Index and the Eysenck Personality Questionnaire—and a set of graphical features of avatar images from the VK online social media that include the pixel characteristics from the HSV and RGB color models and the number of persons and faces depicted in an avatar. The problem is considered from the statistical point of view. The dependency between psychometric scores and the number of faces/persons is assessed with the Kruskal–Wallis test with Dunn test pairwise comparisons. The color-pixel characteristics that are associated with the psychometric scores are selected with several fits of the regularized regression with L2 and MCP penalties. The data for the study were collected via a specially developed application for the online social media platform VK. The results of the analysis support existing research on how colors express personality and discover certain color-pixel image characteristics that could be used for personality computing models.

Segmentation of Leaves and Fruits of Tomato Plants by Color Dominance

The production of food generated by agriculture has been essential for civilizations throughout time. Tillage of fields has been supported by great technological advances in several areas of knowledge, which have increased the amount of food produced at lower costs. The use of technology applied to modern agriculture has generated a research area called precision agriculture, which has providing crops with resources in an exact amount at a precise moment as one of its most relevant objectives The data analysis process in precision agriculture systems begins with the filtering of the information available, which can come from sources such as images, videos, and spreadsheets. When the information source is digital images, the process is known as segmentation, which consists of assigning a category or label to each pixel of the analyzed image. In recent years, different algorithms of segmentation have been developed that make use of different pixel characteristics, such as color, texture, neighborhood, and superpixels. In this paper, a method to segment images of leaves and fruits of tomato plants is presented, which is carried out in two stages. The first stage is based on the dominance of one of the color channels over the other two, using the RGB color model. In the case of the segmentation of the leaves, the green channel dominance is used, whereas the dominance of red channel is used for the fruits. In the second stage, the false positives generated during the previous stage are eliminated by using thresholds calculated for each pixel that meets the condition of the first stage. The results are measured by applying performance metrics: Accuracy, Precision, Recall, F1-Score, and Intersection over Union. The results for segmentation of the fruit and leaves of the tomato plants with the highest metrics is Accuracy with 98.34% for fruits and Recall with 95.08% for leaves.

Utilizing the RGB Color Model and Halftone Technique for Color Segregation in Visual Cryptography

Utilizing the RGB Color Model and Halftone Technique for Color Segregation in Visual Cryptography

Application of Machine Learning to Assess the Quality of Food Products—Case Study: Coffee Bean

Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans.

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region.

Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. Materials and Method. In this study, 200 facial images of diabetes patients with skin conditions such as Bell's palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones' face-detecting algorithm extracts face skin regions from a diabetic person's face image in video frames. The affected skin area on the diabetic person's face is detected using HSV colour model segmentation. The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3%. The facial skin region-based diabetic measurement avoids pricking of the serum and is used for continuous glucose monitoring.

Primary amine supramolecular solvent microextraction for smartphone-based determination of calcium in milk and infant formula

In this work, we proposed a primary amine supramolecular solvent microextraction approach for smartphone-based digital image colorimetry for the first time. We found that amphiphile (primary amine) acted as an alkaline agent for the calcium colored complex formation with 1-(2-pyridylazo)-2-naphthol in deproteinized food sample. After coacervation agent (electrolyte) addition, a spontaneous supramolecular solvent phase separation and calcium complex microextraction took place. We constructed a lab-made colorimetric box to capture the image of a vial containing two separated liquid phases by the smartphone without extract transfer into a cuvette. We used color component values in RGB (red, green, blue) color model as analytical signals for the calcium quantification. The developed microextraction approach provided effective calcium separation from food sample matrices (extraction recovery 97%) and satisfactory selectivity for its determination by the inexpensive, simply operated portable smartphone device. Under optimal conditions, the limit of detection was 40 mg L−1. The calibration plot was linear over the range of 120–5000 mg L−1, which enabled calcium determination in real milk and infant formula samples.

Underwater image enhancement utilizing adaptive color correction and model conversion for dehazing

Underwater imaging is typically affected by absorption and scattering, which can lead to color distortion and image blurring. However, no universal color constancy algorithm for correcting underwater images exists due to the wide variation in underwater scenes. Furthermore, methods based on learning to improve underwater image quality often suffer from a lack of ground truth, which can compromise the validity and authenticity of restored images. In this study, we propose an adaptive underwater image enhancement technique that corrects image color and reduces haze using a valid dataset with ground truth. Specifically, we first use hue channel statistics from underwater images to build a dataset of color-corrected images from different underwater scenes. We use this dataset to train an Unet-like network for adaptive color correction. After color correction, underwater images often exhibit characteristics similar to hazy terrestrial images. We refer to this phenomenon as “model conversion”. The hazy terrestrial images have corresponding ground truth. Therefore, we train a Transformer-like network with hazy terrestrial image datasets to remove haze in underwater images. Our method is more robust for different underwater scenes and elegantly solves the problem of lacking ground truth. A series of experiments demonstrate that our method achieves superior performance compared to state-of-the-art methods in terms of both visual quality and quantitative metrics.

Fundamental scales of hue appearance and discrimination

AbstractHue can be described with two types of scales. The first, for hue discrimination, is of the form of a hue angle metric such as found in the Munsell and CIELAB systems. The second, for hue appearance, is in the form of a hue quadrature metric as found in the NCS system and color appearance models such as CIECAM16. Any useful hue metric should also exhibit hue constancy – the important concept that stimuli of various lightness and saturation, but identical hue designations appear to be of the same hue. Hue angle in the IPT color model has repeatedly been shown to very well describe contours of constant hue and has been rigorously tested over more than two decades. However, the IPT model was not designed with hue spacing, for either discrimination or appearance, in mind. This paper describes the derivation of a fundamental and physiologically plausible model, called FHS for Fundamental Hue Scales, with predictors for both hue discrimination and hue appearance built directly from cone fundamentals and with hue linearity as good as that found in IPT. Such a model can be used with individual LMS color matching functions as the basis for the hue dimension of improved, and individualized, color appearance scales.

Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model

In recent decades, the need for advanced systems with good precision, low cost, and high-time response for wildfires and smoke detection and monitoring has become an absolute necessity. In this paper, we propose a novel, fast, and autonomous approach for denoising and tracking smoke in video sequences captured from a camera in motion. The proposed method is based mainly on two stages: the first one is a reconstruction and denoising path with a novel lightweight convolutional autoencoder architecture. The second stage is a specific scheme designated for smoke tracking, and it consists of the following: first, the foreground frames are extracted with the HSV color model and textural features of smoke; second, possible false detections of smoke regions are eliminated with image processing technique and last smoke contours detection is performed with an adaptive nonlinear level set. The obtained experimental results exposed in this paper show the potential of the proposed approach and prove its efficiency in smoke video denoising and tracking with a minimized number of false negative regions and good detection rates.

Lightweight real-time hand segmentation leveraging MediaPipe landmark detection

Real-time hand segmentation is a key process in applications that require human–computer interaction, such as gesture recognition or augmented reality systems. However, the infinite shapes and orientations that hands can adopt, their variability in skin pigmentation and the self-occlusions that continuously appear in images make hand segmentation a truly complex problem, especially with uncontrolled lighting conditions and backgrounds. The development of robust, real-time hand segmentation algorithms is essential to achieve immersive augmented reality and mixed reality experiences by correctly interpreting collisions and occlusions. In this paper, we present a simple but powerful algorithm based on the MediaPipe Hands solution, a highly optimized neural network. The algorithm processes the landmarks provided by MediaPipe using morphological and logical operators to obtain the masks that allow dynamic updating of the skin color model. Different experiments were carried out comparing the influence of the color space on skin segmentation, with the CIELab color space chosen as the best option. An average intersection over union of 0.869 was achieved on the demanding Ego2Hands dataset running at 90 frames per second on a conventional computer without any hardware acceleration. Finally, the proposed segmentation procedure was implemented in an augmented reality application to add hand occlusion for improved user immersion. An open-source implementation of the algorithm is publicly available at https://github.com/itap-robotica-medica/lightweight-hand-segmentation.

An investigation into the relationship between clothing colors and gender stereotyping in children

This paper investigates the relationship between children's clothing color and gender stereotyping, as well as the underlying mechanisms. Sample colors were chosen based on their representativeness and prevalence, and characterized using the HSB (Hue-Saturation-Brightness) color model. Through sensory experiments, it is found that most colors have varying degrees of gender association. The paper explores the general principle of how gender stereotyping is induced from the perspective of the CMY color theory and analyzes the relationship between color brightness, saturation, and gender stereotyping by establishing regressional models. It is also found that the formation of gender stereotypes in children's clothing is related to people's different expectations for the temperament and behavior patterns of children of different genders through perceptual experiments. Understanding the gender tendencies conveyed by colors can offer designers guidance in the application of colors when creating products targeted at specific markets characterized by different gender cognitive inclinations. Besides, the findings of the study will inform future research in the fields of clothing psychology and children's gender cognition education.

EQIRHSI: enhanced quantum image representation using entanglement state encoding in the HSI color model

EQIRHSI: enhanced quantum image representation using entanglement state encoding in the HSI color model

A new steganography technique based on genetic algorithm

As technology grows, it affect our daily lives becomes more penetrating. Our private information may be violated when communications media are used, to achieve information security this paper was based on a new technique of steganography using cryptography, color model and genetic algorithm. First, the secret text was encrypted with the advanced encryption standard (AES) algorithm. Second, the hiding was applied in two stages. First, the cover-image was converted in to the hue saturation intensity (HSI) color model and select one of the models, then divide it into a group of blocks and hiding the text in them by using least significant bit (LSB) method on specified bytes randomly, then using the genetic algorithm that calculates peak signal-to-noise-ratio (PSNR) for all blocks after the hiding process and thus obtain the best value for PSNR for the optimal block. Second, includes the final hiding of all blocks based on the results of the first stage of the best random distribution of bytes according to the results of the genetic algorithm. The PSNR and mean square error (MSE) measures were adopted to prove the accuracy and efficiency of the results.