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Nowadays, people all over the world are facing several problems related to the deadly disease of breast cancer. The research on breast cancer detection using existing techniques shows lesser detection results due to the shortage of medical facilities and manual detection procedures. However, the disease diagnosis becomes very critical when it is detected in the critical or chronic stage. Early detection plays an essential role in the accurate detection and effective treatment of breast cancer, which further minimizes the death rate. Therefore, automated breast cancer detection based on deep learning is proposed in this paper by using histopathological images. The proposed approach involves five steps: image filtering, dual-stage segmentation, feature extraction, feature selection, and classification. Initially, image filtering is performed to execute image resizing, noise elimination, and contrast enhancement. The weighted Guided Image Filtering (Weighted GIF) approach is used for noise removal, and Transformed Optimal Gamma Correction (TOGC) is used for contrast enhancement. To obtain the cellular structures from histology/histopathology images, a dual-stage segmentation using Superpixel Mixed Clustering (SMC) is applied. Then, feature extraction is done by the Gray Level Co-occurrence Matrix with Three-dimensional space (GLCM -3D) and RGB color model to extract texture and color features. Then, the most significant features are selected using Stochastic Diffusion Dynamic Optimization (SDDO). Finally, breast histology images have been classified using Rapid Tri-Attention Residual Dense Capsule Network with Aquila Optimization (Rapid Tri-Net), further categorizing the histology images into various classes. The proposed approach is simulated in the Python platform using BreakHis and BACH datasets and evaluates the performance on the basis of f-measure, recall, accuracy, precision, and specificity. Rapid Tri-Net's performance is related to the recent prevailing framework to attain a fair comparison. As a result, the simulated results clearly showed that the proposed Rapid Tri-Net performed better than the existing approaches. [ABSTRACT FROM AUTHOR]

Edge detection is a crucial issue in computer vision, with convolutional neural networks (CNNs) being a key component in various systems for detecting edges within images, offering numerous practical implementations. This paper introduces a hybrid approach for edge detection in color images using an enhanced holistically led edge detection (HED) structure. The method consists of two primary phases: edge approximation based on parallel convolutional neural networks (PCNNs) and edge enhancement based on social spider optimization (SSO). The first phase uses two parallel CNN models to preliminarily approximate image edges. The first model uses edge-detected images from the Otsu-Canny operator, while the second model accepts RGB color images as input. The output of the proposed PCNN model is compared with pairwise combination of color layers in the input image. In the second phase, the SSO algorithm is used to optimize the edge detection result, modifying edges in the approximate image to minimize differences with the resulting color layer combinations. The experimental results demonstrate that our proposed method achieved a precision of 0.95. Furthermore, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values stand at 20.39 and 0.83, respectively. The high PSNR value of our method signifies superior output quality, showing reduced contrast and noise compared to the ground truth image. Similarly, the SSIM value indicates that the method's edge structure surpasses that of the ground truth image, further affirming its superiority over other methods. [ABSTRACT FROM AUTHOR]

Although it is known (from the observations of medical professionals) that cigarette smoke negatively affects maxillofacial prostheses, especially through staining/discoloration, systematic research in this regard is limited. Herein, the color modifications of M511 maxillofacial silicone, unpigmented and pigmented with red or skin tone pigments, covered with mattifiers, or with makeup and mattifiers, and directly exposed to cigarette smoke, were investigated by spectrophotometric measurements in the CIELab and RGB color systems. The changes in color parameters are comparatively discussed, showing that the base silicone material without pigmentation and coating undergoes the most significant modifications. Visible and clinically unacceptable changes occurred after direct exposure to only 20 cigarettes. By coating and application of makeup, the material is more resistant to color changes, which suggests that surface treatments provide increased protection to adsorption of the smoke components. The dynamic water vapor sorption (DVS) measurements indicate a decrease of the sorption capacity in pigmented versus unpigmented elastomers, in line with the changes in color parameters. [ABSTRACT FROM AUTHOR]

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user's reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user's color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas. [ABSTRACT FROM AUTHOR]

This work demonstrates that the packaging material commonly called "bubble wraps" (Aeroplast) can be leveraged to serve as a probe to visible sunlight exposure. This probe relies on a newly developed layered metal selenide photocatalyst with the general formula (DMAH)2xMnxSn3–xSe6 (DMSe‐1) (x = 1.3–1.7; DMAH+ = dimethylammonium), featuring a narrow bandgap of 0.76 eV, in addition to an indicator dye and a reducing agent. The photochemically sensitive probe is introduced into the air‐filled compartments of bubble wraps and undergoes photocatalytic degradation, resulting in a chromatic response to sunlight exposure. The probe's sensitivity to variable irradiation doses can be adjusted by varying the amount of the photocatalyst. The color intensity correlates with the absorbed irradiation dose, allowing for qualitative assessment by the naked eye or quantitative measurement using the RGB color system. The results obtained from the new probe agree with those obtained from standard sunlight pyranometers (r = 0.98), with an average error of <15%. This suggests that beyond their use as protective coatings, bubble wraps can be successfully repurposed as visible light sensors. Furthermore, this study describes the initial use of metal chalcogenides as visible light probes, potentially paving the way for the development of innovative light‐sensitive materials. [ABSTRACT FROM AUTHOR]

Merleau-Ponty, starting from perception, reaches the visual through the work of Cézanne. The article applies the phenomenologist's essay to demonstrate how unfounded Cézanne's doubt was about his creative potential and the general change in art caused by him. [ABSTRACT FROM AUTHOR]

Simple analytical devices suitable for the analysis of various biochemical and immunechemical markers are highly desirable and can provide laboratory diagnoses outside standard hospitals. This study focuses on constructing an easily reproducible do-it-yourself ELISA plate reader biosensor device, assembled from generally available and inexpensive parts. The colorimetric biosensor was based on standard 96-well microplates, 3D-printed parts, and a smartphone camera as a detector was utilized here as a tool to replace the ELISA method, and its function was illustrated in the assay of TNFα as a model immunochemical marker. The assay provided a limit of detection of 19 pg/mL when the B channel of the RGB color model was used for calibration. The assay was well correlated with the ELISA method, and no significant matrix effect was observed for standard biological samples or interference of proteins expected in a sample. The results of this study will inform the development of simple analytical devices easily reproducible by 3D printing and found on generally available electronics. [ABSTRACT FROM AUTHOR]

This study addressed the need for a more accessible and efficient method of analyzing phenolic content in vegetable oils. The research aimed to develop a method that could be widely adopted by both researchers and industry professionals, ultimately revolutionizing the way phenolic content in vegetable oils is analyzed. This study developed a method of determining the total phenolic content (TPC) in vegetable oils using smartphone image analysis in the RGB color model. The method employed a gallic acid calibration solution and demonstrated exceptional determination coefficients for the RGB colors. The R—red color was selected as the basis for the analyses, and the method was statistically equivalent to standard UV/Vis spectrophotometry. The highest TPC was determined in hemp and olive oils, while the lowest was found in rice bran, grapeseed, and macadamia nut oils. This study concluded that smartphone image analysis, mainly using the R component of the RGB color model, was a superior alternative to traditional spectrophotometric methods for determining the TPC in vegetable oils. This innovative approach could revolutionize phenolic content analysis by providing researchers and industry professionals with a cost-effective, safe, and efficient tool. The estimated limit of detection (LOD) of 1.254 mg L−1 and limit of quantification (LOQ) of 3.801 mg L−1 further confirmed the reliability and comparability of the method. With these findings, it was expected that the method would be widely adopted in the future. [ABSTRACT FROM AUTHOR]

This research aims to develop a mathematical model for estimating the milling degree of milled rice based on fluorescent imaging. The materials used were the Ciherang, IR64 and Mekongga varieties which are widely grown and consumed by Indonesian people. The experiment was conducted by varying the polishing time starting from 0 seconds to 34 seconds with 1 second intervals. Six grains of polished rice were taken to record their images using a camera with 365 nm UV fluorescent ring light. The data collected in this research were milling degree obtained by gravimetric method, milling degree obtained using a milling meter and color values of fluorescent images of milled rice by image processing. The results showed that prediction of milling degree using the RGB color model has the coefficient of determination between 0.8001 - 0.8652, which is considered as potential to be used as a model for estimating the degree of milled rice based on fluorescence images. The RGB color model shows that the image red signal has the highest coefficient of determination compared to green and blue signals. For all of the three varieties in this study, the Ciherang variety has a predictive model equation for the image red signal y = 3.9027x - 429.61, the IR64 variety has a predictive model equation for the image red signal y = 3.7344x - 415.01, and the Mekongga variety has a predictive model equation for the image red signal y = 3.5627x - 388.86. [ABSTRACT FROM AUTHOR]

This study evaluated the feasibility of an automated method to delimit the required area to quantitatively analyze root filling voids and gaps from cross-sectional confocal laser scanning microscopy (CLSM) images. Root canals of maxillary canines were prepared with rotary instruments and filled by lateral compaction technique using gutta-percha and AH Plus sealer. The roots were stored (100% humidity, 37 °C) for a period of 24 h and then transversally sectioned to obtain 2-mm-thick slices from the apical and middle thirds. The areas corresponding to filling materials, gaps, and voids were manually delimited or automatically demarked by ImageJ software after converting the images to the RGB color system. Based on manual and automatic delimitations, the percentages of voids and gaps were calculated. Data of voids and gaps between middle and apical thirds were individually compared by paired t-test. Pearson's correlation test was used to assess the correlation of data between the methods. Irrespective of the method of area delimitation, no difference was observed between the root thirds for both voids and gaps, while the p-values calculated for each method were similar. Almost perfect correlations between the methods were observed for both outcomes. The proposed method to automatically delimit the areas corresponding to filling material, voids, and gaps appears to be a valid method to facilitate the quantitative analysis of defects in root canal fillings using topographic CSLM images. [ABSTRACT FROM AUTHOR]

Food residues are a promising resource for obtaining natural pigments, which may replace artificial dyes in the industry. However, their use still presents challenges due to the lack of suitable sources and the low stability of these natural compounds when exposed to environmental variations. In this scenario, the present study aims to identify different food residues (such as peels, stalks, and leaves) as potential candidates for obtaining natural colorants through eco-friendly extractions, identify the colorimetric profile of natural pigments using the RGB color model, and develop alternatives using nanotechnology (e.g., liposomes, micelles, and polymeric nanoparticles) to increase their stability. The results showed that extractive solution and residue concentration influenced the RGB color profile of the pigments. Furthermore, the external leaves of Brassica oleracea L. var. capitata f. rubra, the peels of Cucurbita maxima, Cucurbita maxima x Cucurbita moschata, and Beta vulgaris L. proved to be excellent resources for obtaining natural pigments. Finally, the use of nanotechnology proved to be a viable alternative for increasing the stability of natural colorants over storage time. [ABSTRACT FROM AUTHOR]

The phenotypical manifestations of asthma among children are diverse and exhibit varying responses to therapeutic interventions. There is a need to develop objective biomarkers to improve the characterization of allergic and inflammatory responses relevant to asthma to predict therapeutic treatment responses. We have previously investigated histamine iontophoresis with laser Doppler flowmetry (HILD) as a potential surrogate biomarker that characterizes histamine response and may be utilized to guide the treatment of allergic and inflammatory disease. We have identified intra‐individual variability of HILD response type among children and adults with asthma and that HILD response type varied in association with racial classification. As laser Doppler flowimetry may be impacted by skin color, we aimed to further validate the HILD method by determining if skin color or tone is associated with observed HILD response type differences. We conducted an observational study utilizing quantification of skin color and tone obtained from photographs of the skin among participants during HILD assessments via the RGB color model. We compared RGB values across racial, ethnic, and HILD response type via the Kruskal‐Wallis test and calculated Kendall rank correlation coefficient to evaluate the relationship between RGB composite scores and HILD pharmacodynamic measures. We observed that RGB scores differed among racial groups and histamine response phenotypes (p < 0.05). However, there was a lack of correlation between the RGB composite score and HILD pharmacodynamic measures (r values 0.1, p > 0.05). These findings suggest that skin color may not impact HILD response variations, necessitating further research to understand previously observed differences across identified racial groups. [ABSTRACT FROM AUTHOR]

Crown diameter is one of the crucial indicators for evaluating the adaptability, growth quality, and ornamental value of garden chrysanthemums. To accurately obtain crown diameter, this study employed an unmanned aerial vehicle (UAV) equipped with a RGB camera to capture orthorectified canopy images of 64 varieties of garden chrysanthemums at different growth stages. Three methods, namely RGB color space, hue-saturation-value (HSV) color space, and the mask region-based convolutional neural network (Mask R-CNN), were employed to estimate the crown diameter of garden chrysanthemums. The results revealed that the Mask R-CNN exhibited the best performance in crown diameter estimation (sample number = 2409, R2 = 0.9629, RMSE = 2.2949 cm). Following closely, the HSV color space-based model exhibited strong performance (sample number = 2409, R2 = 0.9465, RMSE = 3.4073 cm). Both of the first two methods were efficient in estimating crown diameter throughout the entire growth stage. In contrast, the RGB color space-based model exhibited slightly lower performance (sample number = 1065, R2 = 0.9011, RMSE = 3.3418 cm) and was only applicable during periods when the entire plant was predominantly green. These findings provide theoretical and technical support for utilizing UAV-based imagery to estimate the crown diameter of garden chrysanthemums. [ABSTRACT FROM AUTHOR]

Objective To study the conversion model of RGB color space to CIE1976 L*a*b* color space with higher accuracy, which provides some value for the fields of computer color matching, color detection, and color reproduction. Methods The method of three-dimensional look-up table method and back propagation (BP) neural network are proposed, and the effect of the model built under the two methods is evaluated under the calculation of CIE1976 L*a*b* and CIE2000 color difference by NCS color card data. Results Under the calculation of CIE1976 L*a*b* color difference, the average color difference under the four interpolation methods of the three-dimensional look-up table is within 3, and the average color difference of the BP neural network algorithm is 1.8720. Under the calculation of CIE2000 color difference, the average color difference of the four interpolation methods of the three-dimensional look-up table drops within 1, and the average color difference of the BP neural network also shows a downward trend, and the specific value is 1.3449. Conclusions According to the result obtained by the research method, the color difference of the tetrahedral interpolation method is the smallest among the four interpolation methods of the three-dimensional look-up table method under both color difference formulas. Whether it is the three-dimensional look-up table method or the BP neural network, the model obtained by the CIE2000 color difference formula is the best. In general, for the two methods, the BP neural network method is more convenient and faster, and the color difference effect is also desirable. [ABSTRACT FROM AUTHOR]

The objective of this research was to study of the correlation between RGB colour indicators and lead concentration in leafy plants. Cabbage and lettuce crops were watered with 3 levels of Lead Pb-contaminated (2.4 and 6 mg/lit). To distinguish the heavy metal contamination and their impact on vegetative characteristics for plants, the results showed with the levels of poisoning (0,2,4, and 6 mg/lit) showed the maximum value of Hue and vegetative were 0.76. and 0.032, also showed the minimum value for the same indices were 2.15 and 1.51. Also with the levels of poisoning (0,2,4, and 6mg/lit) showed the maximum value of simple red-green ratio and Green-red vegetation index was 1.61. and 0.23, also showed the minimum value for the same indices were 1.28 and 0.12. for Cabbage crops while for lettuce the results showed with the levels of poisoning (0, 2, 4, and 6 mg/lit) showed the maximum value of Hue and vegetative were 0.71. and 0.027, also showed the minimum value for the same indices were 0.41 and 0.024. Also with the levels of poisoning (0,2,4, and 6 mg/lit) showed the maximum value of simple red-green ratio and Green-red vegetation index was 1.65. and 0.43, also showed the minimum value for the same indices were 1.6 and 0.2. Linear regression analysis was performed on the equations to predict the monitoring Hue and vegetative and simple red-green ratio and Green-red vegetation index The red, green, blue band and intensity, the simple blue-green ratio addition to visible atmospherically resistant index simple green leaf and normalized greenblue difference index The RGB-based vegetation index 2 and RGB-based vegetation index 3 at different poisoning levels. The existence of a strong relationship between them and contains a high coefficient of determination. [ABSTRACT FROM AUTHOR]

This study demonstrated the possibility of using the digital image model and Detect The RGB Colour Vegetation Indicators for Cabbage and Lettuce Crop under nitrogen deficiency and water deficiency. For cabbage, the results show the relationship between the vegetation indicators based on colour indicators and the different fertilization levels of cabbage crops, which were at level (50 ETC), indicating that the Hue index and vegetative reached their heights indicators in the fourth level of fertilization (150%) respectively, which amounted to 2.23 and 2.03. While their minimum indicators were the third level of fertilization and amounted to 2.10 and 0.64 respectively. For Lettuce, the results demonstrated the correlation between the color indicators and the fertilization level (0%), which was at level (100% ETC), during the third stage of irrigation. The simple red-green ratio, green-red vegetation index, and visible atmospherically resistant index all reached their maximum indicators on irrigation, amounting to 0.9, 0.84, and 1.07 respectively, while the simple blue-green ratio, green leaf was increasing until it reached 0.22, 0.73, then followed by the normalized green-blue difference whose maximum indicator reached 0.67 in the same period. As a result of irrigation, the RGB-based vegetation indexes 2 and 3 attained their maximum indicators, which were 5.56 and 6.74, respectively. After watering, the Hue index and vegetative indicators attained their respective peak values of 2.23 and 2.81. While their minimum markers were 2.16 and 2, respectively, before irrigation. [ABSTRACT FROM AUTHOR]

Art and design, as a professional field closely linked with market demand, is imperative to reform the teaching mode. In this paper, the concept of constructivism is incorporated into the professional education of art design, and a new teaching framework is established through the guidance of teaching concepts. Secondly, the RGB color model is applied in the teaching of color design, and the beauty of the color scheme is calculated through the Berkhof equation in color harmony so as to improve the color-matching ability of students’ art design. Big data analysis is used to evaluate the students’ design works comprehensively. Finally, taking College H as an example, the effectiveness of computer-aided teaching in graphic arts design is analyzed, and the impact of the method on the employment of graduates is verified. The results show that in the experimental group using computer-assisted teaching and the control group of traditional teaching mode, the average grade difference is 17.91 points, Sig=0.003. The difference is significant, which indicates that computer-assisted teaching significantly improves the students’ art design professional ability. This innovative model has a significant impact on the enhancement of art design majors in colleges and universities and provides a practical path for innovative artistic design teaching.

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. [ABSTRACT FROM AUTHOR]

In the natural environment, complex and changeable meteorological factors can influence changes in the internal physiology and phenotype of crops. It is important to learn how to convert complex meteorological factor stimuli into plant perception phenotypes when analyzing the biological data obtained under the natural field condition. We restored the true gradation distribution of leaf color, which is also known as the skewed distribution of color scale, and obtained 20 multi-dimensional color gradation skewness-distribution (CGSD) parameters based on the leaf color skewness parameter system. Furthermore, we analyzed the correlation between the five corresponding meteorological factors and canopy CGSD parameters of peppers growing in a greenhouse and cabbages growing in an open air environment, built response model and inversion mode of leaf color to meteorological factors. Based on the analysis, we find a new method for correlating complex environmental problems with multi-dimensional parameters. This study provides a new idea for building a correlation model that uses leaf color as a bridge between meteorological factors and plants internal physiological state. [ABSTRACT FROM AUTHOR]

Chicken meat is a highly sought-after food product among various segments of the general population, known for its high nutritional value and easy accessibility. Presently, meat identification is primarily conducted manually, relying on visual inspection or tactile assessment of the meat's color and texture. However, this approach presents several limitations, particularly when consumers lack the discernment to differentiate the quality of chicken meat freshness. This research aims to identify the freshness level of chicken meat using the Support Vector Machine method, employing the extraction of RGB color moment features to determine the freshness of the meat. The feature extraction process involves calculating the percentage of intensity values for R (Red), G (Green), and B (Blue) in each chicken meat image. Based on the image processing results, the percentage of intensity values, particularly in the R and B parameters, can be used as determining factors. The study involves software testing using fresh and non-fresh chicken meat. The developed system can identify the freshness level of fresh chicken meat with an accuracy rate of 71.6% using the linear kernel SVM and 60.5% using the RBF kernel SVM. This research represents a significant step toward the automation of chicken meat freshness assessment, potentially reducing food waste and enhancing food safety in the food industry. Further research and development could improve the system's accuracy and expand its applications in various food quality control settings. [ABSTRACT FROM AUTHOR]

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 L 2 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. [ABSTRACT FROM AUTHOR]

Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable of handling these cases have not yielded optimal results, especially for images with blur and noise distortions. Therefore, this study proposes a system to improve underexposed and overexposed photos, consisting of two different residual attention convolution networks with the CIELab color space as the input. The first model working on the L-channel (luminance) is responsible for recovering degraded image illumination by using residual memory block networks with self-attention layers. The next model based on dense residual attention networks aims to restore degraded image colors using ab-channels (chromatic). A properly exposed image is produced by fusing the output of these models and converting them to RGB color space. Experiments on degraded synthetic images from two public datasets and one real-life exposure dataset demonstrate that the proposed system outperforms the state-of-the-art algorithms in optimal illumination and color correction outcomes for underexposed and overexposed images. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In contrast, rectangles of the trichromat of color make no stripe since the newly combined illuminators reveal white pixel boundaries for the rectangles (Fig. The image seems counterintuitive; that is, the rectangles of the trichromat of light reveal clear stripes of their CMY components on their edges, while the rectangles of the trichromat of color do not. The color rectangles of the trichromat of color have no separation, while rectangles of the trichromat of light have clear stripes of their CMY color components on their edges. The color rectangles of the trichromat of light have no separation,[13] while rectangles of the trichromat of color have clear stripes of their RGB color components on their edges. [Extracted from the article]

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This study addressed the need for a more accessible and efficient method of analyzing phenolic content in vegetable oils. The research aimed to develop a method that could be widely adopted by both researchers and industry professionals, ultimately revolutionizing the way phenolic content in vegetable oils is analyzed. This study developed a method of determining the total phenolic content (TPC) in vegetable oils using smartphone image analysis in the RGB color model. The method employed a gallic acid calibration solution and demonstrated exceptional determination coefficients for the RGB colors. The R—red color was selected as the basis for the analyses, and the method was statistically equivalent to standard UV/Vis spectrophotometry. The highest TPC was determined in hemp and olive oils, while the lowest was found in rice bran, grapeseed, and macadamia nut oils. This study concluded that smartphone image analysis, mainly using the R component of the RGB color model, was a superior alternative to traditional spectrophotometric methods for determining the TPC in vegetable oils. This innovative approach could revolutionize phenolic content analysis by providing researchers and industry professionals with a cost-effective, safe, and efficient tool. The estimated limit of detection (LOD) of 1.254 mg L−1 and limit of quantification (LOQ) of 3.801 mg L−1 further confirmed the reliability and comparability of the method. With these findings, it was expected that the method would be widely adopted in the future.

Food residues are a promising resource for obtaining natural pigments, which may replace artificial dyes in the industry. However, their use still presents challenges due to the lack of suitable sources and the low stability of these natural compounds when exposed to environmental variations. In this scenario, the present study aims to identify different food residues (such as peels, stalks, and leaves) as potential candidates for obtaining natural colorants through eco-friendly extractions, identify the colorimetric profile of natural pigments using the RGB color model, and develop alternatives using nanotechnology (e.g., liposomes, micelles, and polymeric nanoparticles) to increase their stability. The results showed that extractive solution and residue concentration influenced the RGB color profile of the pigments. Furthermore, the external leaves of Brassica oleracea L. var. capitata f. rubra, the peels of Cucurbita maxima, Cucurbita maxima x Cucurbita moschata, and Beta vulgaris L. proved to be excellent resources for obtaining natural pigments. Finally, the use of nanotechnology proved to be a viable alternative for increasing the stability of natural colorants over storage time.

Economic and ecosystem issues associated with beekeeping may stem from bee mites rather than other bee diseases. The honey mites that stick to bees are small and possess a reddish-brown color, rendering it difficult to distinguish them with the naked eye. Objective and rapid technologies to detect bee mites are required. Image processing considerably improves detection performance. Therefore, this study proposes an image-processing method that can increase the detection performance of bee mites. A keypoint detection algorithm was implemented to identify keypoint location and frequencies in images of bees and bee mites. These parameters were analyzed to determine the rational measurement distance and image-processing. The change in the number of keypoints was analyzed by applying five-color model conversion, histogram normalization, and two-histogram equalization. The performance of the keypoints was verified by matching images with infested bees and mites. Among 30 given cases of image processing, the method applying normalization and equalization in the RGB color model image produced consistent quality data and was the most valid keypoint. Optimal image processing worked effectively in the measured 300 mm data in the range 300–1100 mm. The results of this study show that diverse image-processing techniques help to enhance the quality of bee mite detection significantly. This approach can be used in conjunction with an object detection deep-learning algorithm to monitor bee mites and diseases. [ABSTRACT FROM AUTHOR]

The current study strives to provide a haptic attribute space where texture surfaces are located based on their haptic attributes. The main aim of the haptic attribute space is to come up with a standardized model for representing and identifying haptic textures analogous to the RGB model for colors. To this end, a four dimensional haptic attribute space is established by conducting a psychophysical experiment where human participants rate 100 real-life texture surfaces according to their haptic attributes. The four dimensions of the haptic attribute space are rough-smooth, flat-bumpy, sticky-slippery, and hard-soft. The generalization and scalability of the haptic attribute space is achieved by training a 1D-CNN model for predicting attributes of haptic textures. The 1D-CNN is trained using the attribute data from psychophysical experiments and image features collected from the images of real textures. The prediction power granted by the 1D-CNN renders scalability to the haptic attribute space. The prediction accuracy of the proposed 1D-CNN model is compared against other machine learning and deep learning algorithms. The results show that the proposed method outperforms the other models on MAE and RMSE metrics. [ABSTRACT FROM AUTHOR]

Background: Malachite green (MG) and brilliant green (BG) are two synthetic triphenylmethane dyes with applications in the textile and aquaculture industries. They are considered to be environmental contaminants due to their carcinogenic and mutagenic properties. Both dyes have the same bluish-green color in aqueous solutions. Objective: The aim of this study is to develop a colorimetric analysis as a very simple and cost-effective method to determine the residues of MG and BG simultaneously in aqueous industrial samples. Method: This method is based on the alterations in red, blue, green (RGB) color histograms of the dyes in the presence and absence of Triton X-100 micelle. The images of the samples were taken by a digital camera and converted to the RGB color system using MATLAB software. Partial least-squares regression as a powerful chemometrics tool was used for multivariate calibrations and quantitative measurements. The performance of the proposed method was compared with a simple spectrophotometric method as a reference. Results: Relative errors of prediction for colorimetric and spectrophotometric analysis, respectively, in micellar media were 6.56 and 4.61% for MG and 6.38 and 5.24% for BG. The shortest linear ranges for colorimetric and spectrophotometric analysis, respectively, in micellar media were 0.1–10 and 0.5–5 mg/L for MG and 0.1–15 and 0.5–6 mg/L for BG. The recovery percentages obtained from the analysis of the dyes in real samples of fish-pond water and textile wastewater ranged between 91 and 107%. Conclusions: The good correlation between the results of the colorimetric analysis and the spectrophotometric analysis indicates the reliability of the proposed colorimetric method. Also, the results of the relative recovery study showed insignificant matrix effect. Highlights: This study demonstrates the ability of the colorimetric analysis coupled with chemometrics tools for simultaneous determination of the analytes even with nearly identical colors. [ABSTRACT FROM AUTHOR]

Adequate management of agricultural crops requires, among other things, accessible and sufficiently accurate methods for assessing plant nutrition and crop vegetation status and for agricultural production estimation. Sustainable technologies are based on correct decisions, prompt interventions and appropriate works, and correct information in real time, and the obtaining information methods can be simple, accessible, and appropriate in relation to different user categories (e.g., farmers, researchers, decision makers). This study used mineral fertilization (NPK), with 11 experimental variants, to ensure a controlled differentiated nutrition of the wheat plants, "Alex" cultivar. Regression analysis was used to obtain models in estimating wheat production, by methods based on: (a) NPK fertilizers applied (F) in the 11 experimental variants; (b) physiological indices (PI), represented by the chlorophyll content (Chl), and plant nutrition status on the experimental variants, in terms of macroelement content in the leaves, evaluated by foliar diagnosis (Nfd, Pfd, Kfd); (c) imaging analysis (IA) based on digital images of the wheat experimental variants, and calculated indices. A set of models was obtained, with different precision levels and statistical safety: R2 = 0.763, p = 0.013 for the model based on applied fertilizers (NPKF); R2 = 0.883, p < 0.01 for the model based on foliar diagnosis (NPKfd); R2 = 0.857, p < 0.01 for the model based on chlorophyll content (Chl); R2 = 0.975, p < 0.01 for the model based on normalized rgb color parameters (RGB color system); R2 = 0.925, p < 0.01 for the model based on the DGCI calculated index. The model based on applied fertilizers (F model) was tested in relation to wheat production data, for a period of six years, communicated by other studies. Fit degree analysis between predicted yield based on the F model and real yield (six-year average) was confirmed by R2 = 0.717, compared to R2 = 0.763 for the F model in this study. The models obtained in this study, related to the "Alex" wheat cultivar, can be used for other studies, but with a certain margin of error, given the coefficient values, specific to the obtained equations. The approach concept, methods, and models presented can be opportunities for other studies to facilitate their comparative analysis, their adaptation, and/or development in the form of new models that are useful in different studies, research, or agricultural practices, for their integration into crop management strategies. [ABSTRACT FROM AUTHOR]

Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach. [ABSTRACT FROM AUTHOR]

Steganography algorithms are designed by human experts and use rule-based methods to hide the secret data inside a cover medium. Recently, this process has been automated by using Generative Adversarial Networks. However, Generative Adversarial Networks based steganography has certain limitations. These algorithms may not generate steganographic images that are real enough to fool the intruder. Security of steganography degrades as embedding capacity is increased. A new framework for improved steganography based on deep learning is proposed in this paper. The new framework uses color models with Generative Adversarial Networks. Popular color models such as HSV, YCrCb, YDbDr, CIE-XYZ, YIQ, HED, and YUV are analyzed. The proposed framework helps to identify the best color model with Generative Adversarial Networks based steganography. The quality of steganographic images is assessed with three metrics: capacity, security, and invisibility. Extensive experimentation has been carried out with the CelebA dataset which has more than 200 K samples to assess the effectiveness of the proposed framework. The results show that compared with the RGB and other color models, the CIE-XYZ model produces the best results with Generative Adversarial Networks for steganography.The results further show that the proposed framework improves the security of steganography even when the embedding capacity is increased. [ABSTRACT FROM AUTHOR]

Color correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Many approaches have already been proposed by various researchers; however, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein alternate least square (ALS) and root polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to enhance the image quality and make them look realistic. To achieve this objective, the proposed model used the Amsterdam library of object images which contains a picture of single objects captured under various illumination angles and colors. The main contribution of this paper is a hybrid ALS + RP color correction technique, implemented on the dataset image that fixes its color as per the reference image and enhances its quality. The target image is then converted into three color models, i.e., LAB, LUV, and RGB into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB, to prove its supremacy over other models. [ABSTRACT FROM AUTHOR]

Early identification of diseases in crops improves agronomic decision-making and has a positive impact on agricultural production. In this study, we evaluated three machine learning classifiers to identify three diseases in a tomato crop (Solanum lycopersicum) using chromatic characteristics of digital images of leaves, and a computational tool was developed for its practical use. The classifiers were support vector machine (SVM), multilayer perceptron (MLP), and histogram gradient boosting (HGB). The target classes were tomato yellow leaf curl virus (V), the fungus Septoria lycopersici (H), the acarid Tetranychus urticae (A), and healthy leaves (S). The images were preprocessed to eliminate anomalies and the selection algorithm by region was used to obtain pixels of representative color for each target class. The pixels were then transformed from RGB to the HSV color model to create the training database, which consisted of three-color characteristics (H, S and V) and the associated target class. The three classifiers achieved similar prediction performance. According to the Kruskal Wallis test, there were no significant differences (p-value = 0.5117). SVM obtained an overall accuracy (Acc) of 93.3 %, MLP obtained a value of 93.2 %, and HGB of 93.1 %. Moreover, in performance at the class level (diseases), SVM obtained a higher F1 = 96 % in identification of symptoms caused by Septoria lycopersici and a lower F1 = 90 % in identification of symptoms caused by Tetranychus urticae. The computational tool developed, IDENTO v1.0, facilitated identification of the three leaf diseases in tomato based on optimized classifiers and constitutes an option for promoting the use of artificial intelligence in agriculture. [ABSTRACT FROM AUTHOR]

The development of a portable device created by 3D printing for colorimetric and fluorometric measurements is an efficient tool for analytical applications in situ or in the laboratory presenting a wide field of applications in the environmental and food field. This device uses a light-emitting diode (LED) as radiation source and a webcam as a detector. Digital images obtained by the interaction between the radiation source and the sample were analyzed using a programming language developed in Matlab (Mathworks Inc., Natick, MA, USA), which builds the calibration curves in real-time using the RGB colour model. In addition, the entire system is connected to a notebook which serves as an LED and detector power supply without the need for any additional power source. The proposed device was used for the determination in situ of norfloxacin, allura red, and quinine in water and beverages samples, respectively. For the validation of the developed system, the results obtained were compared with a conventional spectrophotometer and spectrofluorometer respectively with a t-test at a 95% confidence level, which provides satisfactory precision and accuracy values. [ABSTRACT FROM AUTHOR]

Soil color is commonly used as an indicator to classify soil and identify its properties. However, color-based soil assessments are susceptible to variations in light conditions and the subjectivity of visual evaluations. This study proposes a novel method of calibrating digital images of soil, regardless of lighting conditions, to ensure accurate identification. Two different color space models, RGB and CIELAB, were assessed in terms of their potential utility in calibrating changes to soil color in digital images. The latter system was determined to be suitable, as a result of its ability to accurately reflect illuminance and color temperature. Linear regression equations relating soil color and light conditions were developed based on digital images of four different types of soil samples, each photographed under 15 different light conditions. The proposed method can be applied to calibrate variations in the soil color obtained by digital images, thus allowing for more standardized, objective, and accurate classification and evaluation of soil based on its color. [ABSTRACT FROM AUTHOR]

Methods of molecular genetic analysis based on the colorimetric biochip technology have shown their effectiveness in identifying antibiotic resistance genes in bacteria. For the quantitative determination of nucleic acids, a comparative study of methods for converting digital color images of biochips into monochrome black-and-white versions using RGB and CMYK color models has been carried out. A 19-mer single-stranded oligonucleotide and two model mRNAs corresponding to the genes of two types of clinically relevant beta-lactamases (CTX-M and NDM) were studied as objects. The widest range of staining intensity and the best analytical characteristics for the determination of all types of studied nucleic acids were obtained using the red channel of the RGB color model. The detection limits were 0.10 ± 0.02 pmol/μL for the 19-mer oligonucleotide, and 3.0 ± 0.2 and 8.0 ± 0.6 amol/μL for mRNA of beta-lactamases CTX-M-116 and NDM-1, respectively. The developed method can be used for the quantification of genes responsible for the multiple resistance of bacteria to antimicrobial drugs. [ABSTRACT FROM AUTHOR]

In order to improve the recall and precision of image retrieval, a visual retrieval method of digital media image features based on active noise is proposed. In this paper, Canny algorithm is used to detect the edge of the image to get the feature information of the edge of the image. The RGB color space model is used to decompose the color information of the image, and the color characteristic information of the image is obtained. Extracting image features to retain the useful information contained in the image as much as possible; In order to facilitate the visual retrieval of image features, reduce the retrieval complexity and further fuse image features, FPCA and ReliefF algorithms are used to reduce the dimensionality of image features, and the active noise control method is used to sharpen the image. After processing the results, a digital media image feature visual retrieval platform is established to realize the visual retrieval of digital media image features. Experimental results show that the proposed method has a high accuracy of over 80% and a high recall rate of 95.2%. [ABSTRACT FROM AUTHOR]

Background: Indocyanine green (ICG) near-infrared fluorescence cholangiography (NIRF-C) is widely used to visualize the biliary tract during laparoscopic cholecystectomy (LC). However, the ICG dose and its dosing time vary in the literature so there is not a standard ICG protocol. The objectives of this descriptive prospective study were to demonstrate that NIRF-C at a very low dose of ICG provides good visualization of the extrahepatic biliary tree while avoiding hepatic hyperluminescence and to assess the surgeon-perceived benefit. Furthermore, another additional aim was quantifying the amount of ICG dye in the liver tissue and biliary tract through a green colour intensity (GCI) analysis according to red green blue (RGB) color model and correlating it to surgeon-perceived benefit. Method: Forty-four patients were scheduled for LC. We recorded demographics, surgical indication, intraoperative details, adverse reactions to ICG, hepatic hyperluminescence, visualization of the cystic duct (CD), the common bile duct (CBD) and the cystic duct–bile duct junction (CDBDJ) before and after dissection of Calot's triangle, operation time, surgical complications and subjective surgeon data. For all procedures, a unique dose of 0.25 mg of ICG was administered intravenously during the anaesthetic induction. ICG NIRF-C was performed using the overlay mode of the VISERA ELITE II Surgical Endoscope in all surgeries. Video recordings of all 44 LC were reviewed. Using a color analysis software, the GCI of CBD versus adjacent liver tissue was calculated using RGB color model. Results: ICG NIRF-C was performed in all 44 cases. The mean operation time was 45 ± 15 min. There were no bile duct injuries (BDIs) or allergic reactions to ICG. The postoperative course was uneventful in all of cases. The mean postoperative hospital stay was 28 ± 4 h. ICG NIRF-C identified the CBD in 100% of the patients, the CD in 71% and the CDBDJ in 84%, with a surgeon satisfaction of 4/5 or 5/5 in almost 90% of surgeries based on a visual analogue scale (VAS). No statistically significant differences were found in the visualization of the biliary structures after the dissection of Calot's triangle in obese patients or with gallbladder inflammation. Furthermore, 25% of patients with a BMI ≥ 30, 27% of patients with a Nassar grade ≥ 3 and 21% of patients with gallbladder inflammation had a VAS score 5/5 compared to 6% of patients with a BMI < 30 (p = 0.215), 6% of patients with a Nassar grade < 3 (p = 0.083) and none of the patients without gallbladder inflammation (p = 0.037). Measured pixel GCI of CBD was higher than adjacent hepatic tissue for all cases regardless of the degree of gallbladder inflammation, the Nassar scale grades or the patient's BMI (p < 0.0001). In addition, a significant correlation was observed between surgeon-perceived benefit and the amount of ICG dye into the CBD according the RGB color model (p < 0.0001). Conclusion: ICG NIRF-C at a very low dose of ICG (0.25 mg of ICG 20 min before surgery) enables the real-time identification of biliary ducts, thereby avoiding the hepatic hyperluminescence even in cases of obese patients or those with gallbladder inflammation. [ABSTRACT FROM AUTHOR]

Glutathione (GSH) plays an important role in living organisms and is often added to food as a food additive, so it is of great importance to accurately and conveniently determine GSH in food. In this paper, a terbium and copper bimetallic metal-organic frameworks (Tb x /Cu 1-x -BTC) was designed for the visualization and detection of GSH in food. Terbium ions in the Tb x /Cu 1-x -BTC provide the background signal and act as an aggregation center. After reduction and aggregation-induced emission (AIE), GSH-modified copper nanoclusters (GSH-Cu NCs) enhance the red fluorescence and keep the green fluorescence unchanged. This results in a color shift of the system from green to orange. Combined with a smartphone, the RGB value of the system color was extracted to realize the visual detection of GSH. Due to the specificity of the reaction, Tb x /Cu 1-x -BTC was able to specifically recognize GSH among GSH structural analogs with very good selectivity, anti-interference ability, sensitivity and accuracy. In addition, using the difference in color and excitation wavelength range between the probe and the reaction product, the probe was applied in the field of information encryption, which further expanded the application scenario of the probe. Therefore, this study provides new ideas for the detection of GSH in food and the application of fluorescence probes in the field of information encryption. [Display omitted] • A novel bimetallic organic framework is employed to detect GSH. • Fluorescence ratiometric signal was introduced to enhance the detection sensitivity. • The visual point-of-care detection is capable of enabling rapid GSH detection. • A novel bimetallic organic framework is employed to information encryption. • The proposed MOF probe has high selectivity and immunity to detect GSH. [ABSTRACT FROM AUTHOR]

[Display omitted] Pesticide residues in food products are a critical issue concerning food safety. Recent studies have increasingly focused on developing selective, rapid, innovative, and environmentally friendly analytical methods for detecting pesticides in foodstuffs. This study presents a novel analytical method utilizing comprehensive two-dimensional liquid chromatography-tandem mass spectrometry for the simultaneous determination of one hundred and thirteen multiclass pesticides in corn products. The method simplifies the process with a single-step solid–liquid extraction and benefits from two-dimensional separation to enhance resolution and minimize matrix effects. The methodology was validated according to European Commission DG-SANTE (SANTE/11312/2021) and European Committee for Standardization (EN 15662, 2018) guidelines, obtaining acceptable analytical parameters. Limits of detection ranged from 0.2 µg/L to 30.2 µg/L and limits of quantification from 0.3 to 91.6 μg/L, all bellow regulatory limits. Accuracy, ranged from 60 to 120 % and precision, estimated by relative standard deviation, was consistently below 15.1 % in all cases. The applicability of the method was demonstrated by analyzing pesticides in five representative corn products (tortilla, corn flakes, corn cake, starch, and polenta). Sixteen pesticides were detected, all within the regulatory residue limits. Three metric tools were applied to assess the analytical process. Environmental sustainability was evaluated using the AGREE metric, while analytical performance and method applicability were assessed with the Red-Green-Blue and Blue Applicability Grade Index models, respectively, yielding promising scores (0.5, ≥ 0.55, and 0.65). These findings and the comparison to previous methods indicated that this approach represents a promising, alternative and user-friendly strategy for pesticide determination, offering effective productivity and reliable analytical performance. [ABSTRACT FROM AUTHOR]

Point clouds are now a standard way of describing objects in many engineering disciplines, whether they are man-made objects such as structures, buildings, or various types of structures. Commonly used methods of acquiring such data include ground, UAV, or even aerial photogrammetry, followed by terrestrial, UAV, and aerial scanning. After measurement (by the scanner) or calculation (from photogrammetry), the point cloud goes through extensive processing that essentially transforms the unordered mass of points into a usable data set. One of the important steps is removing points representing obstructing objects and features, including vegetation in particular. Here, many filtering methods based on different principles are available and suitable for application to different scenes. This paper presents a new method of filtering point clouds based on the visible spectrum color principle using vegetation indexes determined from RGB system colors only. Since each sensor has to some extent, an individual interpretation of the colors, it cannot be assumed to determine specific boundaries of what is and is no longer vegetation. Therefore, it was proposed to use means clustering to simplify the operator's work. The method was also designed in such a way that the entire evaluation could be implemented in the freely available CloudCompare software. The procedure was tested on three different sites with different terrain and vegetation characteristics showing, which demonstrated the applicability of this method to data where the color information (green) uniquely identifies vegetation. The selected vegetation filters ExG, ExR, ExB, and ExGr were tested, where ExG was the best. Kmeans clustering helps an operator to distinguish more easily between vegetation and the rest of the point cloud without compromising the quality of the result. The method is practically implementable using the freely downloadable and usable CloudCompare software. [ABSTRACT FROM AUTHOR]

Medical optical imaging, with the aid of the "terahertz tomography", is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis for better verification and validation. Two important aspects, the analytic approach for the understanding of the Schrodinger transforms and machine learning approaches for the understanding of the medical images segmentation, are presented in this manuscript. While developing an AI algorithm for complex datasets, the computational speed and accuracy cannot be overlooked. With the passage of time, machine learning approaches have been further modified using the Bayesian, genetic and quantum approaches. These strategies have boosted the efficiency of the machine learning, and specifically the deep learning tools, by taking into account the probabilistic, evolutionary and quantum qubits hypothesis and operations, respectively. The current research encompasses the detailed analysis of image segmentation algorithms based on the evolutionary approach. The image segmentation algorithm that converts the color model from RGB to HSI and the image segmentation algorithm that uses the clustering technique are discussed in detail, and further extensions of these genetic algorithms to quantum algorithms are proposed. Based on the genetic algorithm, the optimal selection of parameters is realized so as to achieve a better segmentation effect. [ABSTRACT FROM AUTHOR]