Hue Component Research Articles

A spatial hue smartphone-based colorimetric detection and discrimination of carmine and carminic acid in food products based on differential adsorptivity

A novel, portable, disposable, affordable, and environmentally friendly paper-based analytical device (PAD) was designed for on-site determination of carmine and carminic acid. This platform utilized paper test strips with a chitosan coating as an adsorption layer, which was characterized using scanning electron microscope, energy-dispersive X-ray analysis, and water contact angle measurement. Carmine and carminic acid could be efficiently adsorbed on chitosan-coated paper test strips, producing distinct colors that could be captured using a smartphone camera without the need for an elution step. Notably, by utilizing the Hue component of the HSL model, it was possible to differentiate between carmine and carminic acid, confirming their presence in a sample. Furthermore, the color saturation intensity changed in a concentration-dependent manner, allowing for the determination of carmine and carminic acid concentrations in the ranges of 200–800 μg/mL and 20–100 μg/mL, respectively. Additionally, the created test strip could be used to measure the percentage of carminic acid in the presence of carmine. The developed PAD enabled the quantification of carmine in various food samples without the need for reagents or complex equipment. The environmental impact of this method was found to be positive based on assessments using GAPI and AGREE tools.

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.

Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex

Aiming at solving the problems of local halo blurring, insufficient edge detail preservation, and serious noise in traditional image enhancement algorithms, an improved Retinex algorithm for low-light mine image enhancement is proposed. Firstly, in HSV color space, the hue component remains unmodified, and the improved multi-scale guided filtering and Retinex algorithm are combined to estimate the illumination and reflection components from the brightness component. Secondly, the illumination component is equalized using the Weber–Fechner law, and the contrast limited adaptive histogram equalization (CLAHE) is fused with the improved guided filtering for the brightness enhancement and denoising of reflection component. Then, the saturation component is adaptively stretched. Finally, it is converted back to RGB space to obtain the enhanced image. By comparing with single-scale Retinex (SSR) algorithm and multi-scale Retinex (MSR) algorithm, the mean, standard deviation, information entropy, average gradient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) are improved by an average of 50.55%, 19.32%, 3.08%, 28.34%, 29.10%, and 22.97%. The experimental dates demonstrate that the algorithm improves image brightness, prevents halo artifacts while retaining edge details, reduces the effect of noise, and provides some theoretical references for low-light image enhancement.

Detection of White Blood Cells Using Algorithms that Analyze the Binary Properties of Red, Blue, and Hue Components

In this paper, a unique method for the identification of white blood cells (WBCs) is proposed. The system is based on morphological analysis and makes use of the red, blue, and hue components of digital pictures. The purpose is to design a technology that is both accurate and efficient for the detection of white blood cells, which is essential for a variety of medical diagnosis. Preprocessing of the pictures, segmentation of white blood cells (WBCs), feature extraction based on morphological features, and classification using machine learning approaches are all components of the procedure. Method: Increasing contrast and removing noise are the first steps in the proposed technique, which starts with picture preparation. In order to separate white blood cells (WBCs) from the background, the next step is to conduct segmentation, which involves a mix of thresholding and morphological processes. After the white blood cells have been segmented, characteristics such as area, perimeter, circularity, and intensity are retrieved from them. These attributes are given into a machine learning classifier, trained on a dataset of labeled WBC pictures, to discriminate between WBCs and other cells or artifacts. Python as well as OpenCV libraries are used in the implementation of the technique. Result: In the process of identifying white blood cells (WBCs) from microscopic pictures, the suggested algorithm yields encouraging results. In the identification of white blood cells (WBC), the evaluation on a dataset consisting of a variety of blood samples reveals good accuracy, sensitivity, and specificity. With regard to both accuracy and computing efficiency, the algorithm's performance is superior to that of other approaches that are currently in use. Moreover, the system demonstrates a high level of stability when confronted with differences in picture quality and staining processes. Conclusion: In conclusion, the algorithm that was created offers a dependable and automated method for the detection of white blood cells (WBC) based on the identification of morphological characteristics in digital pictures. Through the use of the red, blue, and hue components, it does an excellent job of distinguishing white blood cells from other cellular components. The precision and efficiency of the approach make it acceptable for incorporation into clinical workflows, which will help in the identification of a variety of blood illnesses in a timely and accurate manner using the method. It is possible that further improvements and validation on bigger datasets might make its use in clinical settings easier, which would thus contribute to improvements in patient care and diagnosis.

Plant Disease Detection using Deep Learning in Banana and Sunflower

In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.

Deep learning for automated fish grading

Fish is a staple food around the globe, and its quality is heavily dependent on freshness. The conventional method for evaluating the quality of fish is through a visual inspection of a sample. However, this approach heavily depends on human senses for precise assessment, leading to a susceptibility to variability in accuracy and efficiency. Moreover, the potential for safety to be compromised due to errors in the evaluation process makes it a less reliable method. This work presents two Neural Network (NN) architectures, FishNET-S and FishNET-T, to evaluate the quality of the Indian Sardinella and the Yellowfin Tuna, respectively, using RGB images captured from smartphone cameras. The FishNET-S is based on the VGG-16 with the introduction of a Block Attention Module (BAM) to drive the network towards learning the features related to fish quality evaluation from the eye region of the entire fish. In contrast, the FishNET-T architecture first performs a color decomposition based on hue, saturation, and intensity transformations before forwarding the hue and saturation components to the CNN in order to effectively identify grades through fish meat. Experimentally, FishNET-S has managed to obtain an accuracy of 84.1%, while FishNET-T yielded an accuracy of 68.3%. The comparison analysis carried out with the use of generic machine learning and state of the art deep learning models shows that the performance of the proposed novel architectures is dominant and unchallenged.

A dichotomy color quantization algorithm for the HSI color space

Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color quantization algorithms for the Hue Saturation Intensity (HSI) color space with a simple uniform quantization algorithm. In this paper, we propose a dichotomy color quantization algorithm for the HSI color space. The proposed color quantization algorithm can display images with a smaller number of colors than other quantization methods of RGB color space. The proposed algorithm has three main steps as follows: first, a single-valued monotonic function of the Hue (H) component in the from RGB color space to HSI color space (RGB-HSI) color space conversion is constructed, which can avoid the partition calculation of the H component in the RGB-HSI color space; second, an iterative quantization algorithm based on the single-valued monotonic function is proposed; and third, a dichotomy quantization algorithm is proposed to improve the iterative quantization algorithm. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results.

부유선별공정 모니터링을 위한 다변수 가우시안 혼합모델기반 색상 분석

Froth flotation is a type of mineral processing that selectively separates useful minerals from theBRfroth produced by pulp aeration. The physical features of the froth contain information to understandBRthe status of the process of froth flotation. In particular, by obtaining the color feature of the froth, itBRis possible to predict not only the type of useful mineral but also the grade of the mineral. Therefore,BRit is very important to obtain high-precision color analysis results in froth flotation monitoring. In thisBRpaper, we propose a color analysis method based on multivariate Gaussian mixture models to guaranteeBRthe cyclicity with a period of 360 degrees in hue component of color. In order to verify the proposedBRmethod, we present the color analysis results of images which contain around 0º (360º ) of hue andBRimages obtained by froth flotation. Through the experimental results, it shows that the color analysisBRresults obtained by the proposed method are more precise.BR

Menghitung Jumlah Sel Goblet Usus Ayam Secara Otomatis Dengan Metode Multilevel Thresholding

Goblet cell was an indicator the health of chicken intestine that was strongly influence of chicken productivity. So far, the determination of goblet cell was conducted by visual used of microscope. These methods required a long time and also the result was subjective value. The aim of this study was image processed for calculating the number of goblet cells in the chicken intestine and assessed the method applied performance. The material used was 30 images of broiler intestine preparations with AB-PAS staining. The research was carried out used multilevel thresholding and labeling methods for calculating the number of goblet cells in intestine of chicken. Chicken intestinal tissue that had been prepared with AB-PAS staining was acquired used a microscope equipped with a camera and connected to a PC. The result of the acquisition was an RGB scale image, so needed to be converted into an HSV scale image. Next, conducted the extraction process the hue component to convert the image to a gray scale. The gray scale image can be used in the process of identifying goblet cells using the multilevel thresholding method. Objects in the form of goblet cells that have been separated from other tissues were then repaired using morphological operations. The results of morphological operations were then calculated using the labeling method. The results showed that the method applied was successful in automatically calculated the number of goblet cells. The results of calculations using the multilevel thresholding and labeling method had accuracy rate of 90% compared with the results of calculated of visually directly by analyst.

Hue-indexing-based absolute phase retrieval method using a discrete hue sequence

To achieve an effective phase unwrapping for hue-based fringe projection profilometry, this paper proposes a hue-indexing-based absolute phase retrieval method using a discrete hue sequence. First, the hue component is extracted as the wrapped phase for 3D reconstruction by projecting a programmed hue fringe pattern. Afterward, a hue-indexing sequence with a random combination of six unique hues from the hue map is designed for hue unwrapping. By assigning a hue index of the fringe geometric center, the defocusing effect in the hue unwrapping is corrected, where the fringe order of the wrapped hue is then uniquely identified. The simulations show that the root mean square (RMS) of the residual error is 2.2185×10-4 r a d, and the effectiveness of the proposed method is further verified through experiments using a plaster statue and a compressor blade. The measurement comparison of the proposed method and the coordinate measuring machine is provided, where the RMS error of the measured deviation is 4.09×10-2 m m.

Improved Cam Shift Algorithm for Detecting Athletes' Action Targets in Sports Videos

The detection and tracking of athletes in sports videos is of great importance, as it helps to automate the analysis of sports videos, thus providing advanced tools and instruments for sports training. The Cam Shift algorithm uses the colour information of objects to achieve tracking of moving targets, so it is very important to choose a suitable colour space when obtaining the colour information of objects. To this end, this paper improves the Cam Shift algorithm by converting the visual system mechanism to perceive the colour characteristics of the image processing, i.e., converting the video image from the RGB colour space to the HSV colour space, using the H (hue) component to model the target object. Experiments show that the improved athlete detection and tracking algorithm is more robust in practical applications. The improved tracking algorithm also has better real-time performance, with a processing speed of 20 fps during the experiments.

Image features for quality analysis of thick blood smears employed in malaria diagnosis

BackgroundThe World Health Organization (WHO) provides protocols for the diagnosis of malaria. One of them is related to the staining process of blood samples to guarantee the correct parasite visualization. Ensuring the quality of the staining procedure on thick blood smears (TBS) is a difficult task, especially in rural centres, where there are factors that can affect the smear quality (e.g. types of reagents employed, place of sample preparation, among others). This work presents an analysis of an image-based approach to evaluate the coloration quality of the staining process of TBS used for malaria diagnosis.MethodsAccording to the WHO, there are different coloration quality descriptors of smears. Among those, the background colour is one of the best indicators of how well the staining process was conducted. An image database with 420 images (corresponding to 42 TBS samples) was created for analysing and testing image-based algorithms to detect the quality of the coloration of TBS. Background segmentation techniques were explored (based on RGB and HSV colour spaces) to separate the background and foreground (leukocytes, platelets, parasites) information. Then, different features (PCA, correlation, Histograms, variance) were explored as image criteria of coloration quality on the extracted background information; and evaluated according to their capability to classify images as with Good or Bad coloration quality from TBS.ResultsFor background segmentation, a thresholding-based approach in the SV components of the HSV colour space was selected. It provided robustness separating the background information independently of its coloration quality. On the other hand, as image criteria of coloration quality, among the 19 feature vectors explored, the best one corresponds to the 15-bins histogram of the Hue component with classification rates of > 97%.ConclusionsAn analysis of an image-based approach to describe the coloration quality of TBS was presented. It was demonstrated that if a robust background segmentation is conducted, the histogram of the H component from the HSV colour space is the best feature vector to discriminate the coloration quality of the smears. These results are the baseline for automating the estimation of the coloration quality, which has not been studied before, but that can be crucial for automating TBS’s analysis for assisting malaria diagnosis process.

Content‐based image retrieval for super‐resolutioned images using feature fusion: Deep learning and hand crafted

AbstractAn innovative image retrieval agenda by concatenating deep learning features from GoogleNet and low‐level features from HSI and RGB color space is proposed in this article. Most of the CNN features suffer from loss of information due to image resize as a pre‐processing stage. To reduce this information loss super‐resolution technic is used for resizing images. An improved form of dot‐diffused block truncation coding is used for extracting RGB handcraft features. To discover the interdependencies between color and intensity component of an image, interchannel voting between hue, saturation, and intensity component is calculated as a color feature in HSI space. Histogram of orientated gradient (HOG) feature is used as shape feature. Five standard performance parameters, average precision rate, average recall rate, F‐Measure, Average Normalized Modified Retrieval Rank, and Total Minimum Retrieval Epoch, are applied on nine image datasets: Corel‐1K, Corel‐5K, Corel‐10K, VisTex, STex, ColorBrodatz and three subsets of ImageNet dataset for evaluation process of proposed method. For all dataset the best performance is achieved by the proposed method with respect to all performance parameters.

Advanced Retinex-Net image enhancement method based on value component processing

When capturing images under low-light lighting conditions, the resulting images often suffer low visibility. Such low-visibility images not only affect the visual effect but also cause many difficulties in practical application. Therefore, image enhancement technology under low-light conditions has always been a challenging problem in image algorithms. Considering that most of the existing image enhancement methods are based on the RGB color space enhancement technology, the correlation among the RGB three primary colors is ignored, which makes the color distortion phenomenon easy to occur when the image is enhanced. To solve the problems of poor image visibility and color deviation under low-light conditions, in this paper an advanced Retinex network enhancement method is proposed. In the method, firstly the low-light RGB image is transformed into HSV color space, the Retinex decomposition network is used to decompose and enhance the value component separately, and thus increasing the resolution of the value component through up-sampling operation; then, for the hue component and saturation component, the nearest neighbor interpolation is used to increase their resolutions, and the enhanced value component is combined to convert back to RGB color space to obtain the initial enhanced image; finally, the wavelet transform image fusion technology is used to fuse with the original low-light image to eliminate the over-enhanced part in the initial enhanced image. The analysis of experimental results shows that the method proposed in this paper has obvious advantages in brightness enhancement and color restoration of low-light images. Especially, comparing with the original Retinex network method, the NIQE value decreases by an average of 19.49%, and the image standard deviation increases by an average of 41.35%. The algorithm proposed in this paper is expected to be effectively used in the fields of security monitoring and biomedicine.

Systematic segmentation method based on PCA of image hue features for white blood cell counting.

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.

Image analysis of real-time classification of cherry fruit from colour features

An image analysis algorithm for the classification of cherries in real time by processing their digitalized colour images was developed, and tested. A set of five digitalized images of colour pattern, corresponding to five colour classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the coloured area of the fruit. A histogram analysis was carried out for the RGB and HSV colour spaces, where the Red and Hue components showed differences between each of the specified colour patterns of the exporting reference system. This information led to the development of a hybrid Bayesian classification algorithm based on the components R and H. Its accuracy was tested with a set of cherry samples within the colour range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm showed a 100% effectiveness in classifying a sample set of cherries into the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, thus ensuring an affordable commercial deployment.

Efficient nucleus segmentation of white blood cells mimicking the human perception of color

AbstractIn this study, we present a nucleus segmentation proposal of white blood cells (WBCs) using chromatic features. It is human inspired on perception of color: a person locates the nucleus of the WBCs by the chromatic contrast between the nucleus and the other elements of the blood smear. To implement that, we segment the nucleus by selecting the pixels with high chromatic variance. First, an unsupervised neural network, which was trained offline to recognize different colors is applied to the images. Thereby, the hue of the pixels is normalized, and the chromatic variance is accurately computed. Processing the hue and using the unsupervised neural network the brightness and staining variations are robustly estimated. In previous related works, the color components are processed separately as uncorrelated intensity channels, and the mathematical operations are selected intuitively. Unlike that, we use color as a feature without separating the hue components, keeping their correlation, so the formal treat becomes systematic. Experiments use the RGB and HSV spaces with three public image databases: ALL‐IDB2, CellaVision, and JTSC. A pixel‐level segmentation evaluation is performed by comparing the segmented images with the ground truth. Our proposal competes with current methods since the values in accuracy, specificity, precision, sensitivity, dice coefficient, kappa index, and true positive rate all are similar to or improved upon the state of the art. The performance of our approach is classified as excellent regarding the kappa index value, and it detects at least 80% of the cells with an average dice coefficient larger than 0.9.

Content-based image retrieval using feature-fusion of GroupNormalized-Inception-Darknet-53 features and handcraft features

This paper proposed an efficient image retrieval framework by feature-fusion of high-level features from the improved version of DarkNet-53, named GroupNormalized-Inception-Darknet-53 (GN-Inception-Darknet-53), and handcraft features extracted from both RGB and HSI color models. To extract the more detailed features of an image, we augmented one inception layer, which includes 1 × 1, 3 × 3, and 5 × 5 kernels in place of an existing 3 × 3 kernel. To make the normalization process of the proposed model less dependent on batch size, Group Normalization (GN) layer is used instead of Batch Normalization (BN). A modified version of dot-diffused block truncation coding (DDBTC) is used to extract handcraft features in RGB color space. For HSI color space, interchannel voting between hue, saturation, and intensity components is used as color feature. To extract shape features histogram of orientated gradient (HOG) is applied on RGB color space. To evaluate the efficiency of our proposed method, Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are calculated for Corel-1 K, Corel-5 K, Corel-10 K, VisTex, Stex & Color Brodatz datasets. In all datasets, the proposed method shows the best results for all the instances with a minimum average improvement of 7.02%.

Robust Image Hashing Based on Cool and Warm Hue and Space Angle

Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, constructed the cool and warm hue opposite color quaternion (CWOCQ) feature. Then, three-dimensional space, opposite color, and cool and warm hue are combined to obtain the three-dimensional space angle (TDSA) feature. The CWOCQ feature and the TDSA feature are connected and disturbed to obtain the final hash sequence. Experimental results show that the proposed algorithm has good security and has better image classification performance and shorter computation time compared with some advanced algorithms.

Improving data hiding within colour images using hue component of HSV colour space

Improving data hiding within colour images using hue component of HSV colour space