HSV Color Research Articles

Identification of Rusts Formed on Steels by Image Analysis with HSV Color Space

Identification of Rusts Formed on Steels by Image Analysis with HSV Color Space

Retraction Note: A single-qubit-based HSL color model for efficient quantum image security applications

Retraction Note: A single-qubit-based HSL color model for efficient quantum image security applications

Calculation Method Based on Flower Petal Area and Other Plant Leaf Spot Area

In order to calculate the area of irregular shapes such as plant leaves and diseased spots or flower petals, this paper presents a new method to calculate them by using flood fill algorithm, HSV color space, improved k-means algorithm and morphological operation. First, 501 butterfly petal images and pathological leaf images of Bauhinia and phyllotaxus were collected, and then flood was used Fill algorithm selects the disease-free area and records the selected pixel value. HSV color space conversion is applied to the image to facilitate the segmentation of leaves. Then, the improved k-means algorithm is used to extract the binary image of leaves and record the pixel value of the outer contour with morphological closed operation. Finally, the proportion and truth of the disease spots of plant leaves are obtained by calculating the pixel value and the real value of the rectangle in the sampling area Real area. Compared with the results of artificial labeling, the average accuracy of petal area and lesion area of Phalaenopsis was 96.3% and 96.61%, respectively. It can be seen that the program can calculate the area of irregular shape of plant surface accurately. In conclusion, this method can replace the artificial grid method to calculate the information of plant leaf area and disease proportion, and effectively reduce the work intensity of experimental personnel.

Research on the veneer defect image enhancement algorithm based on AMEF-AGC.

Aiming at the veneer defect image acquisition process is prone to the problems of blurred edges, inconspicuous contrast and distortion, which cannot show the defects clearly.To improve image analyzability and clarity, a veneer defect image enhancement method based on AMEF-AGC is proposed herein. First, a veneer defect image is subjected to Gamma correction to obtain multiple underexposed image sequences for which Gaussian and Laplacian pyramids are constructed to determine the weights of the multiple exposure sequence group images.Multiscale fusion is then performed based on these weights. Second, the fused image is converted into the HSV color space, where contrast and brightness enhancements are performed for the luminance component, and then converted back to the RGB color space to obtain an enhanced veneer defect image. Solid wood panels selected herein were pine, poplar and birch with defects including live knots, dead knots, and cracks.Compared with those obtained using several algorithms including AMEF, AGC, improved AGC and GC, this algorithm achieved 6.93% and 5.4% improvements in PSNR and SSIM metrics, respectively.Results demonstrated that the proposed method effectively enhanced veneer defect images with blurred and distorted edges, improved image clarity and visual quality, and made defect parts and details of the image clearer.

Enhancing Underwater Images through Multi-Frequency Detail Optimization and Adaptive Color Correction

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality.

Performance Analysis of a Color-Code-Based Optical Camera Communication System

In this study, we present a visible light communication (VLC) system that analyzes the performance of an optical camera communication (OCC) system, utilizing a mobile phone camera as the receiver and a computer monitor as the transmitter. By creating color channels in the form of a 4 × 4 matrix within a frame, we determine the parameters that affect the successful transmission of data packets. Factors such as the brightness or darkness of the test room, the light color of the lamp in the illuminated environment, the effects of daylight when the monitor is positioned in front of a window, and issues related to dead pixels and light bleed originating from the monitor’s production process have been considered to ensure accurate data transmission. In this context, we utilized the PyCharm, Pydroid, Python, Tkinter, and OpenCV platforms for programming the transmitter and receiver units. Through the application of image processing techniques, we mitigated the effects of daylight on communication performance, thereby proposing a superior system compared to standard VLC systems that incorporate photodiodes. Additionally, considering objectives such as the maximum number of channels and the maximum distance, we regulated the sizes of the channels, the distances between the channels, and the number of channels. The NumPy library, compatible with Python–Tkinter, was employed to determine the color levels and dimensions of the channels. We investigate the effects of RGB and HSV color spaces on the data transmission rate and communication distance. Furthermore, the impact of the distance between color channels on color detection performance is discussed in detail.

Fusion of Color Correction and HSV Segmentation Techniques for Automated Segmentation of Acute Lymphoblastic Leukemia.

This article presents an enhanced segmentation methodology for the accurate detection of acute lymphoblastic leukemia (ALL) in blood smear images. The proposed approach integrates color correction techniques with HSV color space segmentation to improve white blood cell analysis. Our method addresses common challenges in microscopic image processing, including sensor nonlinearity, uneven illumination, and color distortions. The key objectives of this study are to develop a robust preprocessing pipeline that normalizes blood smear images for consistent analysis, implement an HSV-based segmentation technique optimized for leukocyte detection, and validate the method's effectiveness across various ALL subtypes using clinical samples. The proposed technique was evaluated using real-world blood smear samples from ALL patients. Quantitative analysis demonstrates significant improvements in segmentation accuracy compared to traditional methods. Our approach shows strong capability in reliably detecting and segmenting ALL subtypes, offering the potential for enhanced diagnostic support in clinical settings.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Development of ExG, ExR, ExGR, HSV, CIELAB Images from RGB Images Using Image Segmentation Algorithm in Computer Vision Based Herbicide Spraying Applications

Weed management in agriculture is critical for preventing crop yield losses, with traditional methods often leading to environmental harm and increased production costs. This study explores the development of color indices and models for weed detection in computer vision-based herbicide spraying applications. Among the all sensors, RGB colour cameras offer several advantages, including low cost and wide availability of image processing libraries tailored for RGB image analysis. In present study a Logitech C270 webcam was used for acquiring the RGB images and a specially python algorithm was developed for image segmentation based on color. Excess Green (ExG), Excess Red (ExR), Excess Green minus Red (ExGR), HSV, and CIELAB color model images are developed by using algorithm. The research demonstrates that while ExG and ExGR indices are effective under specific conditions, the CIELAB model offers superior segmentation results across varying lighting environments. Among all these color indices and models, particularly CIELAB, can enhance the precision of weed detection in automated herbicide applications, thereby reducing environmental impact and improving agricultural efficiency.

Dynamic assessment of visual fatigue during video watching: Validation of dynamic rating based on post-task ratings and video features

People watching video displays for long durations experience visual fatigue and other symptoms associated with visual discomfort. Fatigue-reduction techniques are often applied but may potentially degrade the immersive experience. To appropriately adjust fatigue-reduction techniques, the changes in visual fatigue over time should be analyzed which is crucial for the appropriate adjustment of fatigue-reduction techniques. However, conventional methods used for assessing visual fatigue are inadequate because they rely entirely on post-task surveys, which cannot easily determine dynamic changes. This study employed a dynamic assessment method for evaluating visual fatigue in real-time. Using a joystick, participants continuously evaluated subjective fatigue whenever they perceived changes. A Simulator Sickness Questionnaire (SSQ) validated the results, which indicated significant correlations between dynamic assessments and the SSQ across five items associated with symptoms associated with visual discomfort. Furthermore, we explored the potential relationship between dynamic visual fatigue and objective video features, e.g., optical flow and the V-values of the hue/saturation value (HSV) color space, which represent the motion and brightness of the video. The results revealed that dynamic visual fatigue significantly correlated with both the optical flow and the V-value. Moreover, based on machine learning models, we determined that the changes in visual fatigue can be predicted based on the optical flow and V-value. Overall, the results validate that dynamic assessment methods can form a reliable baseline for real-time prediction of visual fatigue.

Comparison Classification Of Tomatoes Ripeness Based On RGB, HSV And CMYK Colors Based On Correlation Coefficient

This article discusses the classification of tomato fruit maturity based on color space. Several studies have been conducted to measure maturity levels using RGB and HSV color spaces. In this article, researchers classify the ripeness of tomatoes using the CMYK color space, which researchers have never done before. Next, the classification results of the CMYK color space are compared with the RGB and HSV color spaces. The CMYK color space is a secondary color commonly seen by the human eye. CMYK colors are colors produced from a combination of RGB colors. Comparison of classification results based on CMYK, RGB, and HSV color spaces was carried out using the correlation coefficient and mean square error (MSE). The correlation coefficient is a method that is often used to measure the similarity between 2 images, where the closer to 0 the correlation value, the better

CLASSIFICATION OF X-RAY AND CT IMAGES IN DIFFERENT COLOR SPACES USING ROBUST CNN

Since deep learning models have been successfully used in many fields, they have been used to identify sick and healthy people in X-ray or Computed Tomography (CT) chest radiology images. In this study, Covid-19 and pneumonia classification is performed on both X-ray and CT images using the robust Convolutional Neural Network (CNN). BGR, HSV, and CIE LAB color space transformations are applied to X-ray and CT images to show that the model performs a successful classification independent of dataset characteristics. The binary classification accuracy rates of Covid-19 and pneumonia for X-ray images and CT images are 98.7% and 98.4%, 97.6% and 99.4%, respectively. Precision, Recall, Specificity, F1 score, and Mean Squared Error metrics are calculated for each X-ray and CT dataset. In addition, 5-fold cross-validation proved accuracy of the model. Although X-ray and CT chest radiology images are transformed into different color spaces, the proposed model performed a successful classification. Thus, even if the image characteristics of the radiology device brands change, the computer-based system will be able to make successful disease diagnoses at low cost where expert personnel are insufficient.

Canopy coverage of wheat measured with high accuracy using the HSV colour model and relative depth estimation model, MiDaS

ABSTRACT Canopy coverage calculated from 2D images effectively estimates several parameters, such as dry weight above the ground, stem number, and risk of lodging, without destroying the crops. This study aimed to clarify how to accurately calculate canopy coverage, regardless of environmental conditions, using various colour models and a relative depth estimation model, MiDaS. For wheat cultivated in 2022/23, the canopy coverage at the initiation of the stem elongation stage (CC30), calculated using only the HSV colour model, was overestimated because of the presence of weeds. However, when the heatmap generated using MiDaS was combined with the results extracted from the HSV colour model, it showed a CC30 similar to the accurate CC30 calculated by hand drawing, regardless of the wheat variety, sowing pattern, and location of the image taken, with an RMSE as low as 3.48. The CC30 calculated by combining the HSV colour model and MiDaS was significantly positively correlated with the number of stems, dry weight above ground, and nitrogen content above ground at the initiation of the stem elongation stage of ‘Sanukinoyume 2009’ grown in 2021/22 and 2022/23 (n = 72, r > 0.798). The combination of the HSV colour model and MiDaS effectively and accurately calculates canopy coverage regardless of environmental conditions and can be used for growth diagnosis and variable nitrogen fertiliser application after the initiation of the stem elongation stage.

Combining UAV Multi-Source Remote Sensing Data with CPO-SVR to Estimate Seedling Emergence in Breeding Sunflowers

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R2 is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding.

How to Make Flower Borders Benefit Public Emotional Health in Urban Green Space: A Perspective of Color Characteristics

The emotional health benefits of urban green space have been widely recognized. Flower borders, as a perennial plant landscape, have gradually become a current form of plant application in urban green spaces due to their rich color configurations. However, the related research primarily focuses on the impact of urban green spaces on public health, with relatively little attention given to how the colors of flower borders affect public emotional health. This study explored the relationship between the flower borders color characteristics and the public emotional health. In this study, 24 sample images were used as experimental materials, which selected based on their color richness and harmony. Additionally, face recognition technology and online random questionnaires were utilized to measure the public basic emotions and pleasure, respectively. The result shows that, based on the HSV color model and expert recommendations, 19 color characteristics were identified. The correlation analysis of the results from the public emotion with these color characteristics revealed that 13 color characteristics correlated with public emotional pleasure. Among them, blue, neutral purple, and low saturation were positively correlated. Through factor analysis, these thirteen color characteristics were summarized and categorized into four common factors (F1–F4), three of which are related to color. They are “low saturation of blue-violet percentage” (F1), “color configuration diversity” (F2), “bright red percentage” (F3), and “base green percentage” (F4), with F1 having the largest variance explained (27.88%). Finally, an evaluation model of color characteristics was constructed based on the variance explained by these four factors, which was demonstrated to effectively predict the level of public emotional pleasure when viewing flower borders. The results shed light on the effects of color characteristics on public emotions and provide new perspectives for subsequent flower border evaluations. Our results provide a valuable reference for future flower border color design, aiming to better improve public emotional health.

Multi-color space features and data regression neural network-based method for molten salt temperature detection in rare earth electrolytic processes

To address the online monitoring of molten salt temperature in the rare earth electrolysis process, this paper proposes a rare earth molten salt temperature detection model (MSTDM) based on multi-color space features and data regression neural networks. The model initially preprocesses the collected molten salt temperature images using techniques such as the Gaussian Mixture Model (GMM) and median filtering, followed by color space transformation. Subsequently, 19 hue features used for temperature quantification are extracted, including the channel responses of RGB, HSV, Lab, and Gray color spaces. Finally, a novel data regression neural network (CNN-BiLSTM-Attention) is employed to learn the long-term dependencies between molten salt hue features and temperature, predicting future molten salt temperatures. Experimental results indicate that using the GMM algorithm to filter out flame interference, introducing novel temperature prediction features, and constructing the CNN-BiLSTM-Attention network all enhance the performance of MSTDM in molten salt temperature prediction tasks. The model achieves a mean absolute error of 3.16 °C in predicting molten salt temperatures at rare earth smelting sites. This research not only provides an effective solution for online monitoring of molten salt temperatures in the rare earth electrolysis process but also offers a reliable reference for temperature monitoring in other fields.

Biocompatible sensors for ammonia gas detection

In this work, three different dyes have been tested for the determination of gaseous ammonia. This gas is one of the products of microbial degradation and therefore its presence is an indicator of deterioration and could be used as a food freshness indicator.Three different sensors have been prepared and tested, two of them using the natural pigments curcumin and anthocyanin and the other one using bromothymol blue. All of them are biocompatible and therefore allowed to use in contact with food.Different compositions, materials for deposition, stability and reversibility for ammonia gas detection have been studied under high humidity conditions simulating real packaged food conditions. Colorimetry is the technique used to obtain the analytical parameter, the H coordinate of the HSV colour space, simply using a camera, avoiding the use of complex instrumentation.Sensibility, toxicity grade and stability found show that the sensor could be implemented in packaged food and form the basis of a freshness indicator for the food industry.

Color Analysis of Brocade from the 4th to 8th Centuries Driven by Image-Based Matching Network Modeling

To achieve the color matching rules for the textiles discovered during Silk Road excavations between the 4th and 8th centuries, this research proposed an image-based matching network modeling method. The Silk Road facilitated trade and cultural exchange between the East and West, and the textiles found along the way depict the development of fabrics in a color scheme with great cultural significance. A total of 165 images with brocade patterns were collected from a book with a detailed description of the Western influences on textiles along the Silk Road. Two different clustering methods, including the K-means clustering method and octree quantization approach, were used to extract the primary and secondary colors. By combining the HSV color space with the PCCS color system, the color distribution was analyzed to discover the features of representative color patterns. The co-occurrence relationship of the auxiliary colors was explored using the Apriori algorithm, and a total of eight association rules were established. The results showed that the K-means clustering algorithm can show a better effect of color classification to obtain three primary colors and nine secondary colors. The matching mechanism with a visualized network model was also proposed, which showed that reddish-yellow tones are the main colors in the brocade patterns, and the light and soft tones separately account for 27% and 20%. Beige and brown are the most common colorways, with a confidence level of 47%. One style of brocade pattern was used to demonstrate different appearances within various color networks, which could be applied to 3D virtual fitting. This image-based matching network modeling approach makes the color matching schemes visible, and can assist fashion design with fabric features influenced by historical and cultural development.

Wearable photonic crystal double network hydrogel sensor based on structural color analysis

Abstract This paper introduces a high-toughness photonic crystal (PhCs) dual-network hydrogel sensor designed for mechanical sensing, which enables quantitative analysis of structural color using the HSB color space. The hydrogel achieves a maximum tensile strain of 250% under a tensile stress of 3.5 MPa, thanks to its dual-network structure comprising a covalently cross-linked polyacrylamide (PAM) network and an ionically cross-linked sodium alginate (SA) network. At an 80% tensile strain, the PAM-SA photonic crystal hydrogel displays a blue shift in the bandgap wavelength of over 130 nm and demonstrates a sensitivity of 1.69 nm/%. The analysis of the force-induced color change in the PAM-SA photonic crystal hydrogel utilized both RGB and HSB color spaces. In the HSB color space, the hue component (H) exhibited a strong linear correlation with strain (R2>0.95), indicating the feasibility of quantitative structural color analysis using HSB. As a wearable sensor, the PAM-SA photonic crystal hydrogel precisely detects human motion via bandgap displacement (R2=0.989) and structural color change (R2=0.978). The PAM-SA PhCs hydrogel, featuring easily accessible color information and high sensitivity, has broad potential applications in wearable devices and mechanical sensors.

A 3D U-NET BASED ON EARLY FUSION MODEL: IMPROVEMENT, COMPARATIVE ANALYSIS WITH STATE-OF-THE-ART MODELS AND FINE-TUNING

Multi-organ segmentation is the process of identifying and separating multiple organs in medical images. This segmentation allows for the detection of structural abnormalities by examining the morphological structure of organs. Carrying out the process quickly and precisely has become an important issue in today's conditions. In recent years, researchers have used various technologies for the automatic segmentation of multiple organs. In this study, improvements were made to increase the multi-organ segmentation performance of the 3D U-Net based fusion model combining HSV and grayscale color spaces and compared with state-of-the-art models. Training and testing were performed on the MICCAI 2015 dataset published at Vanderbilt University, which contains 3D abdominal CT images in NIfTI format. The model's performance was evaluated using the Dice similarity coefficient. In the tests, the liver organ showed the highest Dice score. Considering the average Dice score of all organs, and comparing it with other models, it has been observed that the fusion approach model yields promising results.