Color Space Research Articles

The Srgb Color Space Based Density Analysis for Brain Tumor Segmentation

One of the important areas of study in medical image processing is early disease detection and diagnosis. A subfield of medical image processing includes the brain tumour segmentation procedure. Medical professionals have an efficient means of diagnosing illnesses because to computer vision and machine learning technology. The Srgb based density analysis is used in this study to isolate the area of brain tumours in MRI images. To differentiate the tumour area from the surrounding area, the intensity values of the input are normalised using the Gaussian filter and the Srgb colour space. In brain MRI samples, the adaptive threshold approach aids in locating potential tumour space. By calculating region parameters like area and density function, the actual space occupied by brain tumours is recovered. Ultimately, by applying morphological functions and removing potential false positives, the accurate tumour space is identified. Recall, accuracy, and F-measure are a few of the performance indicators that are used to evaluate how effective the suggested strategy.

Binary Yellow Supergiants in the Magellanic Clouds. I. Photometric Candidate Identification

Recent works have constrained the binary fraction of evolved populations of massive stars in local galaxies such as red supergiants and Wolf–Rayet stars, but the binary fraction of yellow supergiants (YSGs) in the Hertzsprung gap remains unconstrained. Binary evolution theory predicts that the Hertzsprung gap is home to multiple populations of binary systems with varied evolutionary histories. In this paper, we develop a method to distinguish single YSGs from YSG plus O- or B-type main-sequence binaries using optical and ultraviolet photometry, and then apply this method to identify candidate YSG binaries in the Magellanic Clouds. After constructing a set of combined stellar atmosphere models, we find that optical photometry is, given typical measurement and reddening uncertainties, sufficient to discern single YSGs from YSG+OB binaries if the OB-star is at least ∼5M ⊙ for T eff,YSG ∼ 4000 K, but requires a ∼20M ⊙ OB star for YSGs up to T eff,YSG ∼ 9000 K. For these hotter YSG temperatures, ultraviolet photometry allows binaries with OB companions as small as ∼7M ⊙ to be identified. We use color–color spaces developed from these models to search for evidence of excess blue or ultraviolet light in a set of ∼1000 YSG candidates in the Magellanic Clouds. We identify hundreds of candidate YSG binary systems and report a preliminary fraction of YSGs that show a blue/UV color excess of 20%–60%. Spectroscopic follow-up is now required to confirm the true nature of this population.

Underwater image color restoration based on depth estimation

Underwater images are often accompanied by color casts, and color restoration of underwater images remains a challenging problem. To tackle these degradation challenges, we present a depth estimation method based on image segmentation, and completes image color restoration on this basis. Specifically, we introduce image segmentation techniques to partition the image into discrete blocks. Our findings indicate a linear correlation between the average b component value in the Lab color space for each block and the corresponding image depth. Subsequently, the segmented images are sorted by depth to facilitate the identification of image blocks that are optimal for estimating backscatter. Local lighting estimation is then performed on these blocks to calculate the image transmission map, thereby completing the color restoration of the underwater image. The color restoration achieved through this method outperforms some advanced techniques. Our image color restoration method also demonstrates good accuracy and stability across different datasets.

Effect of Enamel Surface Coating on Staining Capability Due to Iron-Containing Supplements on Primary Teeth: An in Vitro Study

ABSTRACT Background: The staining of primary teeth due to iron-containing supplements is a common concern among parents and healthcare providers. Materials and Methods: Forty extracted primary teeth were divided into two groups of 20 each. Group A served as the control and received no enamel coating, while Group B was treated with a commercially available enamel surface coating. Both groups were then exposed to iron-containing supplements for 7 days. The teeth were assessed for color changes using a spectrophotometer before and after exposure. The degree of staining was quantified using the CIELAB color space values, specifically the ΔE* value, which indicates the change in color. Results: The mean ΔE* value in Group A (control) was 12.5 ± 1.8, indicating significant staining. In contrast, Group B (coated) showed a mean ΔE* value of 4.2 ± 1.1, reflecting minimal staining. The difference between the two groups was statistically significant (P < 0.001), demonstrating the effectiveness of the enamel surface coating in reducing staining caused by iron supplements. Conclusion: Enamel surface coatings significantly reduce the staining effect of iron-containing supplements on primary teeth.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

Classification of Corn Diseases and Pests Using Fuzzy Naïve Bayes Method

Corn is an essential dietary source for humans and animals. In addition to being a food source, corn has numerous benefits as a manufacturing commodity. The quality of grain crops must be considered to minimise the likelihood of disease and pest infestations. Therefore, the diseases and pests that attack corn plants must be classified so that farmers can control them during the growth period of corn plants. The fuzzy naive Bayes method is a statistical machine learning method that can be used to classify the diseases and pests of corn crops based on colour space-transformed digital images. This study aims to classify corn plant diseases and pests using the fuzzy naive Bayes method. Digital images of corn plant diseases and pests were transformed into a red, green and blue colour space model. The following seven classes of corn plant diseases and pests were classified: leaf rust disease, downy mildew disease, leaf blight disease, Locusta pest, Heliotis armigera pest, Spodoptera frugiperdita pest and non-pathogenic pest. With this method, the classification model achieves an accuracy of 87.83%, a macro precision of 34.91%, a macro recall of 35.90% and a macro f-score of 33.82%.

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.

Novel color space representation extracted by NMF to segment a color image

This paper considers the task of separating pixels in color image into background and foreground classes. Using the machine learning technique known as Nonnegative Matrix Factorization, data pertaining to different color channels – selected by color spaces – are combined, and a novel space representation is extracted.The novel representation of the image includes additional information, namely “metacolor”, which could be related to foreground and background and adopted to improve binary segmentation of the investigated image. In both qualitative and quantitative experiments, the use of novel color space representation produces some improvements in the binary segmentation results when it compared to those obtained applying common simpler thresholding algorithms directly to the original image.

Molecular tool for efficient breeding of DOMINANT Greenshell laying hens and significant refinement of phenotypic selection focused on eggshell color

In recent decades, interest in non-traditionally colored eggs has increased. For breeders, this market interest means breeding lines of laying hens that lay eggs of varied colors, such as the blue-green eggshells (Dominant Greenshell) in this study. This study presents the results of genotyping the polymorphism of the O locus responsible for shell coloration and photometric measurement of eggshell color based on the CIELAb system, which was carried out on the unique Czech breeding population Dominant Greenshell. The aim was to use a combination of phenotyping using the CIELab System method and genotyping of the O locus using the end-point PCR approach with the main focus on the accuracy of distinguishing shell color genotypes, streamlining the selection of dominant homozygotes in the O locus, optimizing this technology for the most efficient and cost-effective selection procedure in practical hen breeding. The optometric method was able to reliably distinguish only dominant and recessive phenotypes and eliminate from the population only undesirable recessive homozygotes with a white colored shell. The parameter a* (redness/greenness) from the CIELab color space turned out to be absolutely key for distinguishing dominant and recessive phenotypes. Using the CART methodology, a classification tree built on discriminating optometric characteristics a-blunt was obtained, however, for the group of desirable O/O homozygotes, the selection approach would result in incorrect genotyping of 31% of individuals. Therefore, a combined approach based on rapid and simple elimination of recessive homozygotes using phenotyping (CIELab photometric measurement) and molecular identification of the EAV-HP insertion in the SLCO1B3 gene in dominant phenotypes, regardless of color intensity affected by laying time/order, and allowing reliable elimination, has proven to be the most effective method to distinguish heterozygotes from the breeding population. The combination of optometric and molecular selection methods then leads to more efficient selection, reduction of overall selection costs. This process led to the stabilization of the breeding population within one generation and the achievement of a pure homozygous line with regard to eggshell color.

Classification of Breast Cancer Tumors from Histopathological Images through a Modified ResNet-50 Architecture

The diagnosis of malignant or benign breast cancer tumors from histopathological images is challenging due to human error, which may lead to the patient undergoing additional, often painful, procedures to collect new data. Utilizing a supervised, pre-trained ResNet-50 model as a second opinion for doctors can help eliminate the need for repeated procedures. One main challenge faced by doctors and machine learning models is image blurriness. Applying various data preprocessing and augmentation techniques, such as resizing, Gaussian blurring, histogram equalization, and color space conversions, can improve the model’s performance. The model achieved its best results with an accuracy of 95.61%, precision of 96%, recall of 94%, and an F1-score of 95%.

Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers.

A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross-sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL-CNN, ML-CNN, and HL-CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56×56 input image size. Among the three types of CNN fusion models, the HL-CNN obtained the best performance, resulting in Rv 2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry.

Cherry Tomato Detection for Harvesting Using Multimodal Perception and an Improved YOLOv7-Tiny Neural Network

Robotic fruit harvesting has great potential to revolutionize agriculture, but detecting cherry tomatoes in farming environments still faces challenges in accuracy and efficiency. To overcome the shortcomings of existing cherry tomato detection methods for harvesting, this study introduces a deep-learning-based cherry tomato detection scheme for robotic harvesting in greenhouses using multimodal RGB-D perception and an improved YOLOv7-tiny Cherry Tomato Detection (YOLOv7-tiny-CTD) network, which has been modified from the original YOLOv7-tiny by eliminating the “Objectness” output layer, introducing a new “Classness” method for the prediction box, and incorporating a new hybrid non-maximum suppression. Acquired RGB-D images undergo preprocessing such as color space transformation, point cloud normal vector angle computation, and multimodal regions of interest segmentation before being fed into the YOLOv7-tiny-CTD. The proposed method was tested using an AGV-based robot in a greenhouse cherry tomato farming facility. The results indicate that the multimodal perception and deep learning method improves detection precision and accuracy over existing methods while running in real time, and the robot achieved over 80% successful picking rates in two-trial mode in the greenhouse farm, showing promising potential for practical harvesting applications.

Design of Chinese painting style classification model based on multi-layer aggregation CNN

This study delves deeply into exploring the artistic value of traditional Chinese painting (TCP) and aims to bridge the gap between its fundamental characteristics and the realm of human emotions. To achieve this, a novel convolutional neural network (CNN)-based classification model for TCP emotions is proposed. By thoroughly analyzing the distinct emotional mapping relationships inherent in TCP, a comprehensive framework is developed. Notably, emotional feature regions are accurately extracted using image saliency, and a multi-layer aggregation recalibrated emotional feature module is seamlessly integrated into the CNN network structure. This integration strengthens the activation of features that significantly influence TCP emotions. Moreover, a method of multi-category weighted activation localization is skillfully employed to classify Chinese painting emotions within the CMYK (cyan magenta yellow black) color space. The empirical results convincingly demonstrate that our algorithm surpasses existing approaches such as VGG-19, GoogLeNet, ResNet-50, and WSCNet in the task of TCP emotion recognition, achieving an impressive accuracy of 92.36%, the largest error is 0.191. This improvement signifies the advancements made by our model in accurately capturing and understanding the emotional nuances within TCP. By outperforming previous methods, our research contributes to the convergence of multimedia technology and cultural education.

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.

Influence of UV irradiation on the luminescence properties of CsPbBr3 perovskite quantum dots and CsPbBr3 perovskite quantum dots/PVDF composite film—for white LED application

Perovskite quantum dots (QDs) have been widely investigated for their excellent properties such as high color purity in displays, tunable emission wavelength, and high photoluminescence quantum yield. For device applications, improving the stability is an area of interest. In this study, the effects of UV irradiation on the structural and luminescence properties of CsPbBr3 perovskite quantum dots (CPB QDs) excited at 365 nm were investigated. To overcome the effects of UV irradiation, a CPB QDs/PVDF composite flexible film was prepared. It exhibits high structural and optical stability under UV irradiation and emits a highly intense green color. The emission wavelength and intensity were observed for three years, and the stability of the temperature-dependent emission intensity up to 400 K has been reported. In addition, it is stable in water. A white LED, fabricated by integrating a blue LED with CPB QDs/PVDF composite film and red phosphor, produces bright natural white light [(CIE x, CIE y) = (0.3704, 0.3611), and CCT = 4177 K] with a color gamut area coverage of 86.4% of the standard NTSC (1953) color space.&#xD.

The Effect of Including Sea Buckthorn Berry By-Products on White Chocolate Quality and Bioactive Characteristics under a Circular Economy Context

The by-products of the extraction of sea buckthorn (Hippophae rhamnoides L.) concentrated juice may represent a functional food ingredient for white chocolate production, as a rich source of bioactive compounds. The effects of six treatments derived from the factorial combination of two types of by-products (with oil or without oil) and three different concentrations (5%, 10%, and 15%), were assessed on rheological, quality, colour, antioxidant, and mineral properties of chocolate. The 15% addition of full powder led to the highest values of max firmness, total shear energy, shear energy, cohesiveness, gummosity, dry matter, and ABTS, compared to the untreated control, but the two highest concentrations of the oil-deprived powder resulted in the protein content increasing. The full powder addition always raised fat levels. Both the ‘L’ and ‘a’ colour component as well as total carotenoids, β-carotene, lycopene, and vitamin C increased with the rise of H. ramnoides powder addition, compared to the untreated control. The opposite trend was shown by the ‘b’ colour component and pH, whereas polyphenols and antioxidant activity attained higher values with the oil-deprived powder. The content of potassium decreased upon the 15% addition of the Hippophae ramnoides by-product powder, compared to the untreated control, whereas calcium and magnesium increased. The 15% H. ramnoides full powder elicited the augmentation of phosphorus content in chocolate, compared to the untreated control, contrary to the effect of the oil-deprived powder on P and Zn. The employment of SBB by-products highlights the great potential for manufacturing innovative functional foods with high nutritional value, such as chocolate.

Image enhancement for dichromatic vision by geometric approach in RGB color space

AbstractThe goal of this study is to process digital images to facilitate color discrimination in dichromatic vision without changing their appearances in trichromatic vision as much as possible. Therefore, we propose a simple and effective image enhancement method using the geometric properties of the RGB color space. Concretely, a novel image representation that clarifies the relationship between dichromatic and trichromatic visions in the RGB color space, and an image enhancement method based on this representation are presented. The effectiveness of the proposed method is verified by experiments using several digital images.

Probiotic Milk Enriched with Protein Isolates: Physicochemical, Organoleptic, and Microbiological Properties.

Incorporating plant protein isolates into milk can enhance probiotic culture growth by providing essential nutrients and altering the physicochemical properties of fermented milk. This study investigated the effects of adding 1.5% or 3.0% soy, pea, and whey protein isolates on the growth of Lacticaseibacillus casei and Lactobacillus johnsonii monocultures, as well as the physicochemical (acidity, syneresis, color) and organoleptic properties of fermented milk during 21 days of refrigerated storage. The results showed that 1.5% SPI and WPI did not significantly alter milk acidity compared to controls. Still, pH increased with 1.5% and 3.0% PPI. Storage time significantly affected pH in L. casei fermented milk. The initial addition of WPI at 1.5% and 3.0% reduced syneresis in L. casei fermented milk compared to other samples. Color components were significantly influenced by isolates. Initial L. casei cell counts were lower with SPI (LCS1.5 and LCS3) and 1.5% PPI (LCP1.5) compared to controls. Increasing isolate concentration from 1.5% to 3% enhanced L. johnsonii growth in WPI-milk but reduced L. casei in LCW3 compared to LCW1.5. Only increased pea protein concentration significantly increased L. casei growth. Probiotic populations generally were reduced during extended storage. Moreover, isolates impacted milk organoleptic evaluation. This research demonstrates the potential of protein isolates in creating health-promoting and diverse fermented products and offers insights into their interaction with probiotic cultures to advance functional food technologies.