Color Visualization Research Articles

Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method

Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images.

An integrated textile of electrical signal sensing with visual indicators and energy supply for perspiration management

Recent advances in wearable electronics have enabled the development of sweat sensors providing valuable information for healthcare monitoring. However, the limitations of sweat sensors are excessive dependence on external detection systems, the impossible to real-time visual signal transmission, and inadequate perspiration management. Herein, a single- and double-layer interwoven fabric (SDIF) is designed to achieve indicators of color visualization with an output of electrical signal and energy supply. After absorption of electrolyte, the SDIF can be rapidly activated, connected with the concentration, infiltrated volume, and environmental parameters, and the variational color of SDIF can provide visual indicators. The one tissue cycle of SDIF with three-weft intervals maintains a stable output voltage of ≈1.0 V, conducted by twisting, folding, dynamic bending, and reusing. Moreover, serial tissue cycles can be woven into large fabrics by connecting in series and parallel configurations for energy supply. The developed SDIF with an interweaving structural design using industrial-producible weaving technology provides the functionality of sweat adsorption and transportation, monitoring by recognition of color, and electrical signals to improve perspiration management.

A dual-reaction sites fluorescent probe for accurate detection of benzoyl peroxide in food

Benzoyl peroxide (BPO) is widely used as a whitening agent in flour, but excessive intake of BPO will severely endanger human health. To quickly and accurately detect BPO on-site, we have rationally designed a novel fluorescent probe PTPY-BE with dual-reaction sites. PTPY-BE underwent a specific cascade reaction with BPO to achieve high-contrast fluorescence turn-on response along with significant achromic reaction. The probe has high sensitivity, excellent selectivity, strong anti-interference ability and low detection limit (LOD = 0.83 mg·kg−1) for BPO. Furthermore, a portable detection platform was fabricated, which offers the portability and color visualization of traditional test strips and the color recognition of a smart device, enabling on-site visualization and quantitative detection of BPO. This platform has been successfully used to determine BPO in real food samples with good recoveries (93.59% - 107.13%). Therefore, this platform possessed great prospect and potential application for the determination of BPO in food.

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Objective. Photon-counting micro-computed tomography (micro-CT) is a major advance in small animal preclinical imaging. Small molecule- and nanoparticle-based contrast agents have been widely used to enable the differentiation of liver tumors from surrounding tissues using photon-counting micro-CT. However, there is a notable gap in the application of these market-available agents to the imaging of breast and ovarian tumors using photon-counting micro-CT. Herein, we have used photon-counting micro-CT to determine the effectiveness of these contrast agents in differentiating ovarian and breast tumor xenografts in live, intact mice. Approach. Nude mice carrying different types of breast and ovarian tumor xenografts (AU565, MDA-MB-231 and SKOV-3 human cancer cells) were injected with ISOVUE-370 (a small molecule-based agent) or Exitron Nano 12000 (a nanoparticle-based agent) and subjected to photon-counting micro-CT. To improve tumor visualization using photon-counting micro-CT, we developed a novel color visualization method, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand. Main results. Our in vivo experiments confirm the effectiveness of this color visualization approach, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggest that Exitron Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 delivers a more comprehensive tumor enhancement but with significant variance between scans due to its short blood half-time. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based contrast agent (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemical analysis. Significance. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor progression and response to treatments.

Impact of 12-Month Angioscopic Thrombi and Yellow Plaque After Drug-Eluting Stent Implantation.

Coronary angioscopy (CAS) has 2 unique abilities: direct visualization of thrombi and plaque color. However, in the recent drug-eluting stent (DES) era, serial CAS findings after DES implantation have not been fully elucidated. We investigated the impact of CAS findings after implantation of a polymer-free biolimus A9-coated stent (PF-BCS) or durable polymer everolimus-eluting stent (DP-EES).Methods and Results: We investigated serial CAS and optical coherence tomography (OCT) findings at 1 and 12 months in 99 patients who underwent PF-BCS or DP-EES implantation. We evaluated factors correlated with angioscopic thrombi and yellow plaque, and the clinical impact of both thrombi and yellow plaque at 12 months (BTY). The BTY group included 17 (22%) patients. The incidence and grade of thrombi and yellow plaque decreased from 1 to 12 months. Although no patients had newly appearing thrombi at 12 months, 2 DP-EES patients had newly appearing yellow plaque at 12 months. Multivariable analysis revealed HbA1c, minimum stent area, and adequate strut coverage were significant factors correlated with 12-month angioscopic thrombi, and DP-EESs were significantly correlated with 12-month yellow plaque. However, BTY was not correlated with clinical events. The management of diabetes, stent area, and adequate stent coverage are important for intrastent thrombogenicity and polymer-free stents are useful for stabilizing plaque vulnerability.

Artificial intelligence approach to predict ladle aluminium content and to protect its refractory lining

ABSTRACT This research focuses on optimising steelmaking processes, specifically in the Basic Oxygen Furnace (BOF) and ladle operations, with an emphasis on ladle basicity and Aluminium dosage for deoxidation. The steelmaking process, conducted in the Basic Oxygen Furnace, involves oxidation, flux addition, and tapping of purified steel into ladles. Maintaining desired ladle basicity is crucial for high-quality steel production, and deoxidation with Aluminium is essential to prevent Ferro Alloy oxidation. Secondary metallurgy is performed to maintain ladle basicity and Aluminium content in the steel. The study introduces Artificial Intelligence (AI) integration, utilising Random Forest Regression model to predict Aluminium consumption. This AI-based approach enhances ladle basicity optimisation, reduces Aluminium usage, and ensures high-quality steel production. The research explores infrared technology, digital image processing, and colour visualisation for slag prevention during ladle tapping. The development of a Random Forest Regressor model surpasses empirical equations in predicting Aluminium consumption, resulting in superior ladle basicity maintenance. Comparative analysis demonstrates the accuracy of AI-aided ladles, optimising Al2O3 inclusion formation and sustaining ladle basicity. The research concludes by highlighting AI’s benefits, including improved steel quality, reduced secondary metallurgy, and enhanced operational efficiency, positioning AI as a valuable tool for cost-effective, energy-efficient, and reliable high-quality steel production.

Use Of Natural Dyes From Plants In Coloring Woven Yarns

The use of natural dyes as an environmentally friendly alternative to reduce the use of synthetic dyes which have a negative impact on the environment. Natural dyes can be obtained from various natural sources such as plants, animal waste, and minerals, with plants being the most common source. Plant pigments such as anthocyanins, tannins, flavonoids, and chlorophyll provide a variety of colors that can be used for coloring. The natural dyeing process involves four stages: making the dye solution, fabric preparation, dyeing, and fixation. Testing the quality of natural dyes includes color visualization tests, color aging, color differences and fastness to washing. The methodology for writing this article is a literature study, reviewing various related research from 2013 to 2023. The results show that several plants such as Tarum, Coconut, Secang, Noni, Turmeric, and Shallots can produce colors such as dark blue, brownish cream, red , yellow, and brownish orange on fabric. In conclusion, natural dyes not only support environmental sustainability but also offer a safe and low-cost coloring alternative, although there are challenges in the manufacturing and application process of these natural dyes.

Color Authenticity for the Sustainable Development of Historical Areas: A Case Study of Shiquan

Currently, historical areas in China are facing issues of color authenticity and excessive commercialization. Colors, as an important element of historical areas, reflect their history and culture. However, there are some flaws in the existing research methods, so a more scientific and systematic method is needed to obtain accurate and objective color visualization results to promote the sustainable development of historical areas. In order to explore the methods and applications of visualizing the environmental color patterns of historical areas, Shiquan Street in Suzhou, China, was taken as the research object; ArcGIS 10.5 was used to obtain the street-view photo samples; Matlab was used to summarize the architectural colors of the historical areas; and K-means clustering was used to generate the central color card of the historical areas, thus completing the color visualization of the historical areas, followed by further searching for the color patterns and central colors, so as to explore the strategies for sustainable development of historical areas. The various quantified color models of historical areas can explore the regional uniqueness and color locality of historical areas, which avoids problems such as homogenization of block colors, unclear styles and features, and an excessive proportion of commercial colors caused by poor color guidance and control in the design related to historical areas, providing approaches to the research on the harmonious unity of environmental colors and features related to historical areas. A color-based research method has been proposed to protect and maintain the original appearance of historical areas. The application of this method provides a new perspective and possibility for the protection of historical areas, allowing these areas to achieve historical sustainability while maintaining their historical characteristics and values.

Fractal based feature extraction technique for classifying EEG signal for color visualization tasks

Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy (SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features.

Digital visualization in graduate transport phenomena

AbstractThe use of contour flood plots and color digital movies is investigated for teaching fluid mechanics, heat transfer, and mass transfer at the first‐year graduate student level. The color visualizations are intended to not only stimulate curiosity but also encourage enthusiasm and determination in learning difficult material. Although there is a significant amount of finite‐difference material, the focus is on analytical solutions. In particular, the mathematical problems involve error functions and Fourier series, which generally prove uninteresting and unpopular. Students are encouraged to produce the visualizations themselves, in addition to being provided with digital results in various forms. Excel can be used to generate both the analytical and finite difference calculations, but Python or another computer language can be employed. While software, such as Matplotlib with Python, Gnuplot, or Tecplot, can create excellent contour flood plots, results from Excel are also good, and Powerpoint will produce movies from individual frames. Student response is positive, although quantifying their reaction is difficult based on the small class sizes. Not surprisingly, the use of color appeals to students and adds to their interest. In addition, students claim increased understanding with contour plots and movies, but the visualizations should be used in conjunction with more traditional graphs, perhaps augmented with consistent use of color. However, the impact on motivation due to color presentation is harder to establish. Interestingly, both graduate and senior undergraduate students showed about equal preference for the rainbow and viridis color palettes.

A Wearable Fluorescence Imaging Device for Intraoperative Identification of Human Brain Tumors.

Malignant glioma (MG) is the most common type of primary malignant brain tumors. Surgical resection of MG remains the cornerstone of therapy and the extent of resection correlates with patient survival. A limiting factor for resection, however, is the difficulty in differentiating the tumor from normal tissue during surgery. Fluorescence imaging is an emerging technique for real-time intraoperative visualization of MGs and their boundaries. However, most clinical grade neurosurgical operative microscopes with fluorescence imaging ability are hampered by low adoption rates due to high cost, limited portability, limited operation flexibility, and lack of skilled professionals with technical knowledge. To overcome the limitations, we innovatively integrated miniaturized light sources, flippable filters, and a recording camera to the surgical eye loupes to generate a wearable fluorescence eye loupe (FLoupe) device for intraoperative imaging of fluorescent MGs. Two FLoupe prototypes were constructed for imaging of Fluorescein and 5-aminolevulinic acid (5-ALA), respectively. The wearable FLoupe devices were tested on tumor-simulating phantoms and patients with MGs. Comparable results were observed against the standard neurosurgical operative microscope (PENTERO® 900) with fluorescence kits. The affordable and wearable FLoupe devices enable visualization of both color and fluorescence images with the same quality as the large and expensive stationary operative microscopes. The wearable FLoupe device allows for a greater range of movement, less obstruction, and faster/easier operation. Thus, it reduces surgery time and is more easily adapted to the surgical environment than unwieldy neurosurgical operative microscopes. Clinical and Translational Impact Statement-The affordable and wearable fluorescence imaging device developed in this study enables neurosurgeons to observe brain tumors with the same clarity and greater flexibility compared to bulky and costly operative microscopes.

Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images.

Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilized in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualization approach to all of the tests. As a result, the architecture achieves a classification accuracy rate of 0.96, thus producing excellent results.

The people DataViz’s history ignores: a step forward to an intersectional history of Data Visualization

The history of Data Visualization, like history itself, has been told through the works of a narrow demographic group: white, cisgender men from high-income countries. While Florence Nightingale often features in such historical accounts and some recent literature has branched out to explore contributions from people with diverse gender and ethnical identities, a true feminist, intersectional, and decolonized History of Information Visualization is still missing. This paper attempts to jumpstart the effort of looking at women and people of color in Visualization’s past and examine the oppressive structures that hampered the deserved dissemination of their contributions.

A Randomized Clinical Trial Evaluating Indigo Carmine as a Visualization Aid for Evaluating Ureteral Patency

ObjectivesTo determine whether intravenous indigo carmine provides a visualization advantage compared to saline in the evaluation of ureteral patency in a randomized, controlled clinical trial. MethodsPatients undergoing urological or gynecological surgical procedures in which the patency of the ureter was to be assessed received a saline injection and were randomized to receive 2.5 mL or 5.0 mL of indigo carmine. Blinded video assessments were conducted by independent reviewers using a conspicuity scale ranked 1 (poorest) to 5 (best), and subjects with scores ≥ 3 and at least a +1-point difference from saline were considered responders. Time to visualization was recorded for indigo carmine. A responder analysis evaluated whether indigo carmine showed improved visualization. ResultsThere were 96 ureters evaluated with the 5.0 mL dose of indigo carmine, 92 with the 2.5 mL dose, and 180 with saline. Most ureters were scored a 4 or higher on the conspicuity scale following indigo carmine; both doses were significantly better than saline (p<0.0001). Overall, 92.3% of patients were rated as a responder for either ureter. The median time to visualization of blue color was not significantly different (6.0 minutes in the 5.0 mL group and 5.9 minutes in the 2.5 mL group). There were no adverse events related to indigo carmine use. ConclusionsBoth dose levels of indigo carmine were significantly better than saline as a visualization aid for ureter patency.

CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.

Colorful visualization detection of near-infrared light enabled by an upconversion device with a color-tunable quantum dot light-emitting unit

Color-tunable near-infrared (NIR)-to-visible upconversion devices (UCDs) that correlate the NIR power intensity with the visible emission color are highly desired and hold promise for interactive signal visualization in intelligent optoelectronic devices. In this work, solution-processed color-tunable UCDs integrating a NIR sensing photodetector unit and a color-tunable quantum dot (QD) light-emitting unit are demonstrated. We mixed the red and green QDs in a single emissive layer (EML) for multi-color emission from the UCDs, which is quite different from the previously reported work that used multiple EMLs with different colors. The image color of the resulting color-tunable UCDs can be modulated by bias voltage and driving current and shows a wide color-span range from red to green as the NIR intensity increases. Finally, we present a qualitative correlation between the incident NIR power intensity and the visible emission color, which enables colorful visualization detection of NIR light.

Exploration of visual variable guidance in outdoor augmented reality geovisualization

ABSTRACTThe visual perception of augmented reality (AR) geovisualization is significantly different from traditional controllable 2D and 3D visualization. In this study, we extended the rendering styles of color variables to include natural material color (NMC) and illuminating material color (IMC) and extended the size to include linear size (LS) and angular size (AS). Outdoor AR geovisualization user experiments were conducted, examining the guidance characteristics of five static variables (NMC, IMC, shape, AS, LS) and two dynamic variables (vibration, flicker). The results showed that the two dynamic variables provided the highest guidance, and among all the static variables, the order of guidance was shape, IMC, AS, NMC, and finally LS. This is a new finding that is different from the color, size, and shape guidance order in 2D visualization and the color, shape, and size order in 3D visualization. The results could be utilized to guide the selection of visual variables for symbol design in AR geovisualization.

Data treatment methods for real-time colorimetric loop-mediated isothermal amplification reactions

With the SARS-CoV-2 pandemic and the need for affordable and rapid mass testing, colorimetric isothermal amplification reactions such as Loop-Mediated Isothermal Amplification (LAMP) are quickly rising in importance. The technique generates data that is similar to quantitative Polymerase Chain Reaction (qPCR), but instead of an endpoint color visualization, it is possible to construct a signal over a time curve. As the number of works using time-course analysis of isothermal reactions increases, there is a need to analyze data and standardize their related treatments quantitatively. Here, we take a step forward toward this goal by evaluating different available data treatments (curve models) for amplification curves, which allows for a cycle threshold-like parameter extraction. In this study, we uncover evidence of a double sigmoid equation as the most adequate model to describe amplification data from our remote diagnostics system and discuss possibilities for similar setups. We also demonstrate the use of multimodal Gompertz regression models. Thus, this work provides advances toward standardized and unbiased data reporting of Reverse Transcription (RT) LAMP reactions, which may facilitate and quicken assay interpretation, potentially enabling the application of machine learning techniques for further optimization and classification.

Optimasi Level Benzyl Amino Purin (BAP) terhadap Pertumbuhan Tanaman Kembang Telang (Clitoria ternatea) melalui Teknik Kultur Jaringan

The research objective was to measure the optimum level of BAP for supporting the growth of butterfly pea flowers through tissue culture techniques. The study used a completely randomized design (CRD) with 5 treatments based on BAP media levels (BAP 0 ppm, BAP 0.5 ppm, BAP 1 ppm, BAP 1.5 ppm, and BAP 2 ppm) and 20 replications. Parameters measured were plant height, plant height increase, number of leaves, number of branches, number of tillers, percentage of tiller growth, plant weight, and leaf color. The results showed that the use of BAP levels 0, 0.5, 1, 1.5, and 2 ppm in butterfly pea flower plants through tissue culture techniques was not effective in increasing plant height, number of leaves, number of branches, number of tillers, and percentage of tillers at the end of the research (27 DAP). Using an optimum BAP level of 0.5 ppm resulted in higher biomass production and the dominance of green leaf color visualization.&#x0D; Key words: BAP (Benzyl Amino Purine), butterfly pea, Clitoria ternatea, tissue culture

Tri-directional x-ray phase contrast multimodal imaging using one hexagonal mesh modulator.

Objective. X-ray phase contrast imaging is a promising technique for future clinical diagnostic as it can provide enhanced contrast in soft tissues compared to traditional x-ray attenuation-contrast imaging. However, the strict requirements on the x-ray coherence and the precise alignment of optical elements limit its applications towards clinical use. To solve this problem, mesh-based x-ray phase contrast imaging method with one hexagonal mesh is proposed for easy alignment and better image visualization.Approach. The mesh produces structured illuminations and the detector captures its distortions to reconstruct the absorption, differential phase contrast (DPC) and dark-field (DF) images of the sample. In this work, we fabricated a hexagonal mesh to simultaneously retrieve DPC and DF signals in three different directions with single shot. A phase retrieval algorithm to obtain artifacts-free phase from DPC images with three different directions is put forward and false color dark-field image is also reconstructed with tri-directional images. Mesh-shifting method based on this hexagonal mesh modulator is also proposed to reconstruct images with better image quality at the expense of increased dose.Main results. In numerical simulations, the proposed hexagonal mesh outperforms the traditional square mesh in image evaluation metrics performance and false color visualization with the same radiation dose. The experimental results demonstrate its feasiblity in real imaging systems and its advantages in quantitive imaging and better visualization. The proposed hexagonal mesh is easy to fabricate and can be successfully applied to x-ray source with it spot size up to 300μm.Significance. This work opens new possibilities for quantitative x-ray non-destructive imaging and may also be instructive for research fields such as x-ray structured illumination microscopy (SIM), x-ray spectral imaging and x-ray phase contrast and dark-field computed tomography (CT).