White Balance Research Articles

An Eye-tracking Study: Investigating the Impact of Website Theme Colors on Viewer Engagement

The Internet’s rapid evolution has transformed the media landscape, prompting brands and companies to transition from traditional print and TV to online media. Enhancing website design has become a focal point for effectively conveying information and capturing users’ attention. Among the various elements involved, the choice of web page theme color stands out as a crucial visual component in web navigation, significantly impacting the viewer’s user experience. Investigating the influence of web page colors on viewer attention is imperative. This study employs eye-tracking technology to delve into the impact of diverse web page colors on participants’ attention, particularly on news websites. An eye tracker was employed to meticulously record participants’ gaze patterns while browsing by experimenting with new pages featuring distinct theme colors. Data analysis revealed that red and blue colors garnered the most attention from viewers, followed by yellow, while gray-themed sites performed the least effectively in this regard. These findings offer valuable insights for web design improvement. In the context of news websites, red and blue are particularly effective in capturing and guiding people’s attention. When emphasizing or conveying breaking news, incorporating blue or red icons or relevant elements can be a powerful means of communication; however, it is equally important to be mindful of the potential for viewer visual fatigue when using highly saturated colors over a significant area. Therefore, designers must exercise care in determining the color balance when employing high-contrast and high-saturation hues.

Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Humanity currently lives in a technological era that witnesses rapid progress in multiple fields. Digital image processing is one of the modern technologies that has provided practical answers to many challenges including image enhancement, analysis, reconstruction, recovery, compression, processing, and understanding. One of these notable challenges relates to underwater photography. Underwater images are always exposed to less-than-ideal conditions due to environmental and physical factors. These include refraction of light in water, scattering of particles and dust in the aquatic medium, lack of illumination in deep water, and poor contrast. These challenges make it extremely difficult to analyze and extract valuable information without advanced processing. In this study, an improved color balance-fusion algorithm is provided by improving the image visuality and modifying some equations to obtain sharper and clearer images. The proposed algorithm begins by finding the white balance of the input RGB color image, after that, it improves the intensity. Next, the edges are improved using Gamma separately. The weights are then found for each image and combined to find naive fusion. The resulting image is processed using a color retrieval algorithm to produce the final image. along with comparisons to eleven other algorithms with various processing methods. Experimental results showed that this algorithm can significantly improve underwater images, increasing image clarity and making colors clearer. The improvement rates reached 5.8389 and 2.6778 for UISM and UICM metrics, respectively.

Анализ данных аэрофотосъемки с использованием ГИС

The article considers an approach to the construction of complex distributed, implemented on the platforms of fog and edge computing, cyber-physical systems with a high level of architectural dynamics. The article discusses the issues of processing and analyzing images obtained from unmanned aerial vehicles. Orthophotomap and digital terrain model constructing model has been developed and implemented using data received from «Geoscan» LLC. As a result, 7 raster TIFF format orthophoto maps were created with accuracy of up to 0.025 meters. A geodatabase was built in the geographic information system into which the resulting orthophotomaps were imported. Based on the orthophotomaps, a raster mosaic was created, background and the color balance were adjusted. The resulting terrain image was analyzed, a slopes map, surface aspect, hillshade and a realistic image of the terrain were created. Aerial photography data analyzing algorithm, can be applied for processing any images obtained from unmanned aerial vehicles. The results obtained can be used for survey work, land management, architectural and construction design, environmental monitoring, and agriculture.

A dataset of SEM images of atmospheric particles in Qingdao

In this paper, we selected seven representative sampling points in Qingdao to collect atmospheric particulate samples in different periods, and then obtained the SEM (Scanning Electron Microscope) images of particulate matters through scanning electron microscopy. By observing and analyzing the morphological characteristics of the images, we identified and selected 334 SEM images of particles with distinct morphological characteristics. Based on the obtained SEM images of atmospheric particles, the particles are divided into seven categories according to their morphological characteristics: chain particles, flocculent particles, fibrous particles, spherical particles, quasi spherical particles, irregular mineral particles, and regular mineral particles. These obtained SEM images are further enhanced using corresponding image processing methods such as horizontal inversion, color balance, brightness transformation, fuzzy processing, etc. New image samples are generated and added to the dataset, presenting a dataset of SEM images for seven types of atmospheric particles, totaling 2,672 SEM images in total. The dataset can be used for identifying, classifying, segmenting research on atmospheric particles using machine learning and other related methods. Moreover, it can provide fundamental data for other related research on atmospheric particles.

From Rocks to Pixels: A Protocol for Reproducible Mineral Imaging and its Applications in Machine Learning

Identifying minerals is essential for geology, mineral exploration, engineering, and environmental sciences. Recent advances in machine learning have illustrated its potential as a fast, cost-effective, and reliable tool for identifying minerals from photographs or photomicrographs. However, in the recent literature, few studies have been dedicated to image acquisition. Machine learning generally requires reproducible, high-quality data to perform complicated tasks such as mineral identification to avoid common pitfalls. In this paper, we propose a practical image acquisition protocol for optical microscopes. This protocol focuses on ensuring reproducibility and enhancing image quality. To favor reproducibility, we detail dealing with camera errors, using reference color gauges, and establishing experimental parameters such as the external light source and temperature. For image enhancement, we explain the importance of lighting and its impact on machine learning precision, selection of the objective, and white balance calibration. In addition, we trialed the protocol on heavy mineral concentrate from till samples (20 species) with a typical deep learning model and it revealed that minor lighting modification (<5% difference in one channel) significantly increased misclassification rates: kyanite from 6.4% to 24.9% and monazite from 6.5% to 42.9%.

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers. By altering image attributes to produce analogous images that elicit a different prediction by the classifiers, and by asking physicians to identify medically meaningful features in the images, the counterfactual images revealed that the classifiers rely both on features used by human dermatologists, such as lesional pigmentation patterns, and on undesirable features, such as background skin texture and colour balance. The framework can be applied to any specialized medical domain to make the powerful inference processes of machine-learning models medically understandable.

Reshaping yeast metabolism and enhancing the quality of fresh-style red wine through low-temperature fermentation

Fresh-style red wine is attracting consumers, prompting an exploration of low-temperature winemaking techniques for its industrial production. Herein, low temperatures were applied to red winemaking, evaluating metabolite compositions and sensory profiles of wines fermented at lower versus higher temperatures. The accumulation of various phenolics at different temperatures may work synergistically with temperatures to shape yeast metabolism. Low temperatures significantly weakened the extraction of phenolic compounds, thus attenuating the bitterness and astringency of wine and contributing better balance and brighter color to wines at lower temperatures. Esters, especially ethyl acetate (increased by 24.8%), ethyl hexanoate (increased by 72.3%), ethyl octanoate (increased by 67.9%), and ethyl decanoate (increased by 50.6%) increased with decreasing temperatures, contributing fresh red fruit aromas to wines fermented at 10 °C, while phenylethanol (decreased by 60.0%) decreased with decreasing temperatures, weakening floral aromas to wines at 10 °C. Low temperatures can reduce higher alcohols (19.3% in total) and positively influence the sensory quality of wine. In general, wines fermented at 10 °C exhibited the highest overall quality, highlighting the potential of low-temperature fermentation in the production of fresh-style red wine.

FEATURES OF DESIGNING A MODERN OFFICE SPACE, ITS EQUIPMENT AND FURNITURE

The aim of the study is to identify the main factors, modern approaches and methods of creating a comfortable office space based on the analysis of modern domestic and foreign experience in designing this type of premises.
 Methodology. Critical analysis of scientific sources on the research topic, normative and reference literature, comparative analysis of advanced domestic and foreign experience in office interior design, and a synthesis of the research results have been used in the study.
 Results. Historical aspects of the development of offices and modern trends in the interior design of this type of premises are considered. The main factors of creating a comfortable office space are identified: design in accordance with modern ergonomic requirements; implementation of aesthetic principles of interior design, effective functional and planning organisation. The ergonomic criteria for workplace organisation are presented, and the main aspects of office ergonomics are highlighted. Based on the analysis of modern design experience, approaches and techniques of artistic and aesthetic, functional and planning solutions for office spaces are identified. In particular, rational planning that combines common communication spaces with individual workstations, artistic and aesthetic solutions with balanced colour palette and accents, optimal lighting and modern ergonomic equipment are becoming integral components of an effective modern office space.
 The scientific novelty lies in determining the main factors in creating a comfortable office space based on the analysis of modern interior design practice for this type of premises, identifying current trends, approaches and techniques for creating the office interior, which is a further contribution to the development of research on office environment design.
 The practical significance of the results obtained lies in the possibility of their application by designers and employers when designing office interiors.

Analysis for diagnosis of pneumonia symptoms using chest X-ray based on MobileNetV2 models with image enhancement using white balance and contrast limited adaptive histogram equalization (CLAHE)

This study focuses on diagnosing pneumonia symptoms using chest X-ray (CXR) images. It employs the MobileNetV2 model alongside image enhancement techniques, including white balance and CLAHE. MobileNetV2 is a computationally efficient Convolutional Neural Network (CNN) known for its robust image recognition capabilities. White balance corrects color imbalances in CXR images, ensuring color consistency, while CLAHE enhances contrast and image details for improved analysis. The enhanced CXR images are fed into the pre-trained MobileNetV2 model, which distinguishes pneumonia and non-pneumonia cases. The study aims to enhance pneumonia diagnosis accuracy, benefiting from MobileNetV2’s efficiency and image enhancement. Notably, it achieved high accuracy and low loss for both three-class (91.17% accuracy, 35.0% loss) and two-class (99.76% accuracy, 7% loss) classifications, with the best results in the 50-epoch test. However, it is essential to consider the trade-off between computing time and the risk of overfitting when increasing epochs. Future research could explore additional features to further enhance model performance.

Research on the vision system of lychee picking robot based on stereo vision

In this study, we propose a development scheme of litchi robot vision system based on stereo vision. The system includes three components: image acquisition, image analysis, and image processing. The obtained image can be corrected according to the light environment conditions with the corresponding algorithm for image color, calculate the Hue color layer, analyze the external characteristics and positional data (2D) of litchi, obtain the 3D coordinate information of fruits and vegetables, and provide to the running device for route planning and vegetable and fruit picking claw tools for picking. The integration test results for the robot arm and claw tool demonstrate the following: (1) Using image acquisition, the system can acquire high-quality images in real-time via a combination of automatic exposure and white balance conversion. (2) Using image analysis techniques such as background separation, edge detection, and external feature search, it is possible to successfully calculate the location coordinates of the litchi's tip, center, and bottom. (3) Using image processing, the claw of the picking machine can determine the condition of overlapping lychees and their state of ripening based on the depth information of feature points used in the picking operation. (4) Under the working range of 18.4 cm × 33.6 cm, the success rate of picking litchi with a diameter of 28 mm, 34 mm, and 42 mm was 93.3%, 91.1 %, and 84.4%, respectively, exceeding the design expectation level.

Rainbow subgraphs in edge‐colored complete graphs: Answering two questions by Erdős and Tuza

AbstractAn edge‐coloring of a complete graph with a set of colors is called completely balanced if any vertex is incident to the same number of edges of each color from . Erdős and Tuza asked in 1993 whether for any graph on edges and any completely balanced coloring of any sufficiently large complete graph using colors contains a rainbow copy of . This question was restated by Erdős in his list of “Some of my favourite problems on cycles and colourings.” We answer this question in the negative for most cliques by giving explicit constructions of respective completely balanced colorings. Further, we answer a related question concerning completely balanced colorings of complete graphs with more colors than the number of edges in the graph .

A coal and gangue detection method for low light and dusty environments

In practical underground production environments, low light intensity and the presence of dust can disrupt the recognition of coal and gangue. To advance the separation of coal and gangue in underground settings, this paper introduces a coal–gangue recognition approach (DG Module (DGM) + YOLOX-PSB) tailored for conditions of low light intensity and dust. To address the impact of dusty conditions, a preprocessing module (DGM) is proposed. This module employs a dark channel prior dehazing algorithm to mitigate the impact of fog on coal–gangue images. Subsequent steps include white balancing, bilateral filtering, and gamma correction to alleviate noise and distortion issues arising from the dehazing algorithm. To counteract potential drawbacks of the DGM and enhance target recognition accuracy, a polarized self-attention mechanism is integrated during the feature extraction stage to prioritize edge information of coal–gangue targets. By combining the attributes of the weighted bidirectional feature pyramid network, multiple layers of coal–gangue features are efficiently fused to achieve precise identification of coal–gangue targets. Experimental results using a custom dataset demonstrate that the enhanced algorithm outperforms YOLOv3, YOLOv5, YOLOv7-Tiny, and YOLOX, achieving a recognition accuracy of 97.6%, a frames per second rate of 99 and a good smoke concentration robustness. The proposed DGM + YOLOX-PSB serves as a valuable reference for accurate coal and gangue identification in conditions of low light intensity and dust within underground environments.

Image Enhancement Approach for the Underwater Images Using the Optimized Color Balancing Model

Marine resources are one of the valuable resources that need to be explored and protected, but are affected due to environmental factors. The underwater images are distorted due to the light scattering and absorption phenomenon, which draws the attention of the researcher to restoring the underwater images which is a challenging task. High-quality underwater images help us to learn and study deep-sea environments, and also help in gaining deep knowledge about the ocean resources. In this research, deep learning methods are used for obtaining high-quality underwater images. Accordingly, a channel-based white balancing and social situation-aware guard optimization (WBSSGO) dependent multi-variant balancing color correction model is proposed by integrating the grouping behavior and adaptability nature of the guards and flocks, which acts as the search agents in the proposed optimization. A multi-variant-based white balancing model is employed for balancing the channels and correcting the distorted underwater images. The performance of the developed model is validated using PIQE, BRISQUE, and UCIQE, which acquired minimal values of 27.202, 24.692, and 29.725, respectively with comparative methods, while the higher value of SDME is 64.049 dB for the proposed method, which is reported to acquire the higher visual quality of underwater images.

Multimedia image evaluation based on blockchain, visual communication design and color balance optimization

The relationship between image processing and image analysis is inseparable. With the increasing demand for multimedia visual images, the quality of image analysis is also increasing. However, in image processing and computer vision tasks, protecting users' privacy and preventing data leakage and abuse are not handled well. Image enhancement and nonlinear image color balance algorithm are applied to improve the visual quality of multimedia visual images and make them clearer and fuller. The article utilized image enhancement and a non-linear image color balance algorithm to improve the processing effect before visual image analysis. It also utilized the encryption mechanism of blockchain technology to detect the similarity of multimedia visual images. By comparing the feature points of the images, similar images were matched to address the copyright issue of the images. After experimental testing, the effect of image enhancement is significant, and the histogram of image equalization is significantly better than the original image. In the experiment of image analysis, the computer accurately classified visual images with different attributes. Finally, in the similarity detection algorithm of blockchain, the test results showed that when the number of image transactions reaches 500, the difference hash algorithm takes 1.13 s and 0.78 s to calculate the similarity comparison between the original and secondary images. The differential hash algorithm of blockchain is significantly superior to the Message-Digest Algorithm (MD5) in terms of computational speed and resource consumption. It has better image similarity detection performance and can also provide better image copyright protection mechanisms.

Efficient Contrast Adjustment and Fusion Method for Underexposed Images in Industrial Cyber-Physical Systems

Owing to imaging equipment's environment and limitations, the images obtained in industrial cyber-physical systems (ICPSs) are degraded and available in various visual appearances. The process of highlighting the hidden contents in night-time contrast-distorted images is complex. Earlier approaches have solved this problem from a different perspective and achieved remarkable results that are generally unsatisfactory for images with diverse illumination distortions in ICPSs. Hence, an effective visibility enhancement model is proposed to eliminate inconsistent color casts while highlighting more hidden content for improved inspection, safety in large spaces, and monitoring of large systems. Our proposed model has four steps: 1) removal of the unnatural color cast via a white balance technique, 2) use of probability density and softplus functions to process the images with the actual color cast, 3) using an optimization algorithm to estimate the illumination and adjusting it using a nonlinear function, and 4) blending by multiscale fusion to obtain the most effective visual result. Evaluation of ten benchmark datasets using 14 quality metrics for 22 conventional and modern algorithms shows that our approach is robust, flexible, and applicable to numerous vision-based applications, such as ICPSs, autonomous vehicles, smart cameras, smart mobility, and transportation, especially in low-light environments.

White Balance Correction for Detecting Holograms in Color Images of Black-and-White Photographs

White Balance Correction for Detecting Holograms in Color Images of Black-and-White Photographs

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image.

Tracking People Using Face Recognition

In recent years, face recognition has emerged as an active field within computer vision, driven by potential applications and the parallel development of algorithmic techniques alongside the increasing availability of cost-effective computers with sufficient computational power to support these algorithms. As a biometric information process, a face recognition system offers wider applicability and operational range compared to other biometric methods such as fingerprinting, iris scanning, or signature recognition. The system employs a blend of methodologies across two primary domains: face detection and recognition. Face detection is executed on real-time images without a specific application context in mind. Processes involved in the system encompass white balance correction, segmentation of skin-like regions, extraction of facial features, and the extraction of face images from a face candidate. The integration of a face classification method utilizing a Feedforward Neural Network is a key component of the system. The system's performance is evaluated using a database containing 10 individuals with 20 to 30 photos of each person.

Viability of periodontal ligament cells in selected transport media for avulsed teeth

To compare the viability of periodontal ligament (PDL) cells stored in Hanks Balanced Salt Solution (HBSS) with those in readily available transport media over a variable period of time. Methods: Periodontal ligament cells harvested from premolars freshly extracted for orthodontic reasons were cultured for exponential growth. The cells were exposed to egg white, evaporated milk, water and Hanks Balanced Salt Solution (HBSS) at room temperature. Their viability was evaluated after 30 minutes, 1 hour and 3 hours with the tetrazolium salt-based colorimetric assay (MTT assay). Statistical analysis was done using the IBM® SPSS version 23.0 software. Comparison between the Mean Optical Densities (MODs) of the cells stored in HBSS and other media at each time interval was done using the independent t test. Repeated measure ANOVA and Tukey post-hoc test were also carried out to compare the MOD of cells within each medium over time. The level of significance was set at p <0.05. Result: The PDL cells stored in egg white had higher MODs than those in HBSS at 30 minutes and 1 hour. Conversely, the MODs of the cells stored in milk and water were lower than those in HBSS at all the studied points. There was a significant difference in the viability of the cells stored in HBSS and water at all the time points (p<0.05). Conclusion: For up to an hour, egg white was found to perform better than HBSS in supporting the viability of PDL cells.

Improve Image White Balance by Facial Skin Color

Improve Image White Balance by Facial Skin Color