Search Results (374)

Various parameters and observation conditions contribute to the emergence of color. This phenomenon poses a challenge in modern visual communication systems, which are continuously being enhanced through new insights gained from research into specific psychophysical effects. One such effect is the psychophysical phenomenon of simultaneous contrast. Nearly 90 years ago, Kurt Koffka described one of the earliest illusions related to simultaneous contrast. This study examined the perception of gray tone variations in the Koffka ring against different background color combinations (red, blue, green) displayed on a computer screen. The intensity of the effect was measured using lightness difference ΔL₀₀ across light-, medium-, and dark-gray tones. The results were analyzed using descriptive statistics, while statistically significant differences were determined using the Friedman ANOVA and post hoc Wilcox tests. The strongest visual effect was observed the for dark-gray tones of the Koffka ring on blue/green and red/green backgrounds, indicating that perceptual organization and spatial parameters influence the illusion’s magnitude. The findings suggest important implications for digital media design, where understanding these effects can help avoid unintended color tone distortions caused by simultaneous contrast. Full article

This study focuses on the stabilization of a natural hair dye derived from Lawsonia inermis L. (henna) and Indigofera suffruticosa (indigo). Although various formulations already exist, they are designed for immediate use and cannot be stored. Lawsonia, a primary component of the dye, tends to degrade after release. To ensure its stability, freeze-drying was implemented as a protective measure. Colorimetric analysis confirmed the dye’s ability to maintain an intense, uniform coloration even after multiple washing cycles. Stability tests demonstrate that freeze-drying effectively enhances the dye’s stability and capacity to retain its physical properties and color under various environmental conditions, demonstrating its potential for long-term use. The dye’s pH (5.05) aligns with the natural pH of hair, promoting cuticle sealing and improving hair health. Cytotoxicity tests confirmed the dye’s safety, showing no harmful effects. Gray hair exhibited a total color difference (ΔE) of 64.06 after the initial application, using natural gray hair as a reference. By the third application, ΔE increased to 69.86 and gradually decreased to 68.20 after 15 washing cycles, highlighting its long-term durability. Gray hair exposed to 720 h of UV radiation showed a ΔE of 17.34, whereas dyed gray hair exhibited a ΔE of 2.96 compared to non-UV-exposed samples. This indicates superior resistance to color degradation in dyed hair. Also, SEM imaging revealed the dye’s restorative effects, progressively improving hair cuticle structure with each application. Full article

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R² of 0.87 and an RMSE of 1.68, a 23% improvement in R² compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management. Full article

As part of an implementation in the Peruvian textile industry, the use of different sources to obtain blue hues in alpaca fiber has taken on a prominent role. The present study investigated the optimization of the dyeing process of alpaca fibers using indigo carmine as dye. The methodology was based on a central composite design (CCD) and response surface methodology (RSM) with color strength (K/S) as response variable. The results demonstrate that the independent variables significantly affected the color strength (K/S). In this context, an increase in both mordant concentration (3.9887 g/L) and dyeing temperature (95 °C), coupled with lower exhaust time (30.0019 min), enhanced levels of superficial dye adsorption. Additionally, color fastness properties provided tolerable values according to the gray scale. In conclusion, the optimization of the dyeing process of alpaca fibers using indigo carmine enabled the achievement of a blue shade with satisfactory fastness properties in the fiber yarns. Full article

Creating grayscale images from a color reality has been an inherent human practice since ancient times, but it became a technological challenge with the advent of the first black-and-white televisions and digital image processing. Decolorization is a process that projects visual information from a three-dimensional feature space to a one-dimensional space, thus reducing the dimensionality of the image while minimizing the loss of information. To achieve this, various strategies have been developed, including the application of color channel weights and the analysis of local and global image contrast, but there is no universal solution. In this paper, we propose a bio-inspired approach that combines findings from neuroscience on the architecture of the visual system and color coding with evidence from studies in the psychology of art. The goal is to simplify the decolorization process and facilitate its control through color-related concepts that are easily understandable to humans. This new method organizes colors in a scale that links activity on the retina with a system of opponent and complementary channels, thus allowing the adjustment of the perception of warmth and coolness in the image. The results show an improvement in chromatic contrast, especially in the warmth and coolness categories, as well as an enhanced ability to preserve subtle contrasts, outperforming other approaches in the Ishihara test used in color blindness detection. In addition, the method offers a computational advantage by reducing the process through direct pixel-level operation. Full article

Urban color represents the visual skin of a city, embodying regional culture, historical memory, and the contemporary spirit. However, while the existing studies focus on pedestrian-level facade colors, the “fifth facade” from a bird’s-eye view has been largely overlooked. Moreover, color distortions in traditional remote sensing imagery hinder precise analysis. This study targeted 56 Chinese coastal cities, decoding the spatiotemporal patterns of their fifth facade color (FFC). Through developing an innovative natural color optimization algorithm, the oversaturation and color bias of Sentinel-2 imageries were addressed. Several color indicators, including dominant colors, hue–saturation–value, color richness, and color harmony, were developed to analyze the spatial variations of FFC. Results revealed that FFC in Chinese coastal cities is dominated by gray, black, and brown, reflecting the commonality of cement jungles. Among them, northern warm grays exude solidity, as in Weifang, while southern cool grays convey modern elegance, as in Shenzhen. Blue PVC rooftops (e.g., Tianjin) and red-brick villages (e.g., Quanzhou) serve as symbols of industrial function and cultural heritage. Economically advanced cities (e.g., Shanghai) lead in color richness, linking vitality to visual diversity, while high-harmony cities (e.g., Lianyungang) foster livability through coordinated colors. The study also warns of color pollution risks. Cities like Qingdao exposed planning imbalances through color clashes. This research pioneers a systematic and large-scale decoding of urban fifth facade color from a remote sensing perspective, quantitatively revealing the dilemma of “identical cities” in modernization development. The findings inject color rationality into urban planning and create readable and warm city images. Full article

Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under complex or extreme lighting. We propose SCR-AWB, a novel algorithm that leverages real skin reflectance data to estimate the scene illuminant’s SPD and CCT, enabling accurate skin tone reproduction. The method integrates prior knowledge of human skin reflectance, basis vectors, and camera sensitivity to perform pixel-wise spectral estimation. Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. These findings validate SCR-AWB as an effective and computationally efficient solution for robust skin color correction. Full article

Characterizing powder feedstock is crucial for ensuring the quality and reliability of parts produced through metal additive manufacturing (AM). The morphology of particles impacts the flowability, packing density, and spreadability of powders, affecting productivity and part quality. A new methodology has been developed to classify particle morphological features in AM powder feedstocks, such as spherical or elongated shapes, and the presence of satellites and facets. This approach uses multiple descriptors for quantitative evaluation. The results from shape descriptors can vary based on image resolution, gray/color thresholding, and software algorithms. There are various commercial systems available for characterizing particle shape, some of which use images taken of static particles, while others use images of particles in motion. This diversity can lead to differences in powder characterization across laboratories with different equipment and methods. This paper compares results from a particle classification approach using two software programs that work with metallographic images with those from an automated static particle analyzer. While traditional methods offer higher resolution and precision, this study shows that automated systems can achieve similar particle shape classification using different shape descriptors and thresholds. Full article

This paper presents a real-time system for feature detection and description, the first stage in a structure-from-motion (SfM) pipeline. The proposed system leverages an optimized version of the ORB algorithm (oriented FAST and rotated BRIEF) implemented on the Digilent Zybo Z7020 FPGA board equipped with the Xilinx Zynq-7000 SoC. The system accepts real-time video input (60 fps, 1920 × 1080 resolution, 24-bit color) via HDMI or a camera module. In order to support high frame rates for full-HD images, a double-data-rate pipeline scheme was adopted for Harris functions. Gray-scale video with features identified in red is exported through a separate HDMI port. Feature descriptors are calculated inside the FPGA by Zynq’s programmable logic and verified using Xilinx’s ILA IP block on a connected computer running Vivado. The implemented system achieves a latency of 192.7 microseconds, which is suitable for real-time applications. The proposed architecture is evaluated in terms of repeatability, matching retention and matching accuracy in several image transformations. It meets satisfactory accuracy and performance considering that there are slight changes between successive frames. This work paves the way for future research on the implementation of the remaining stages of a real-time SfM pipeline on the proposed hardware platform. Full article

Zoharite (IMA 2017-049), (Ba,K)₆ (Fe,Cu,Ni)₂₅S₂₇, and gmalimite (IMA 2019-007), ideally K₆□Fe²⁺₂₄S₂₇, are two new sulfides of the djerfisherite group. They were discovered in an unusual gehlenite–wollastonite paralava with pyrrhotite nodules located in the Hatrurim pyrometamorphic complex, Negev Desert, Israel. Zoharite and gmalimite build grained aggregates confined to the peripheric parts of pyrrhotite nodules, where they associate with pentlandite, chalcopyrite, chalcocite, digenite, covellite, millerite, heazlewoodite, pyrite and rudashevskyite. The occurrence and associated minerals indicate that zoharite and gmalimite were formed at temperatures below 800 °C, when sulfides formed on external zones of the nodules have been reacting with residual silicate melt (paralava) locally enriched in Ba and K. Macroscopically, both minerals are bronze in color and have a dark-gray streak and metallic luster. They are brittle and have a conchoidal fracture. In reflected light, both minerals are optically isotropic and exhibit gray color with an olive tinge. The reflectance values for zoharite and gmalimite, respectively, at the standard COM wavelengths are: 22.2% and 21.5% at 470 nm, 25.1% and 24.6% at 546 nm, 26.3% and 25.9% at 589 nm, as well as 27.7% and 26.3% at 650 nm. The average hardness for zoharite and for gmalimite is approximately 3.5 of the Mohs hardness. Both minerals are isostructural with owensite, (Ba,Pb)₆(Cu,Fe,Ni)₂₅S₂₇. They crystallize in cubic space group Pm$\overline{3}$m with the unit-cell parameters a = 10.3137(1) Å for zoharite and a = 10.3486(1) Å for gmalimite. The calculated densities are 4.49 g·cm⁻³ for the zoharite and 3.79 g·cm⁻³ for the gmalimite. The primary structural units of these minerals are M₈S₁₄ clusters, composed of MS₄ tetrahedra surrounding a central MS₆ octahedron. The M site is occupied by transition metals such as Fe, Cu, and Ni. These clusters are further connected via the edges of the MS₄ tetrahedra, forming a close-packed cubic framework. The channels within this framework are filled by anion-centered polyhedra: SBa₉ in zoharite and SK₉ in gmalimite, respectively. In the M₈S₁₄ clusters, the M atoms are positioned so closely that their d orbitals can overlap, allowing the formation of metal–metal bonds. As a result, the transition metals in these clusters often adopt electron configurations that reflect additional electron density from their local bonding environment, similar to what is observed in pentlandite. Due to the presence of shared electrons in these metal–metal bonds, assigning fixed oxidation states—such as Fe²⁺/Fe³⁺ or Cu⁺/Cu²⁺—becomes challenging. Moreover, modeling the distribution of mixed-valence cations (Fe²⁺/³⁺, Cu⁺/²⁺, and Ni²⁺) across the two distinct M sites—one located in the MS₆ octahedron and the other in the MS₄ tetrahedra—often results in ambiguous outcomes. Consequently, it is difficult to define an idealized end-member formula for these minerals. Full article

This study aimed to develop a green and sustainable composite polysaccharide gel with antioxidant activity and freshness-monitoring properties. Blueberry pomace was repurposed to extract anthocyanins (BA), which were incorporated into chitosan (CS)/polyvinyl alcohol (PVA) and starch (S)/PVA matrices to prepare pH-indicating composite polysaccharide gels. The anthocyanin solution exhibited significant colorimetric responses to pH 2–14 buffer solutions. Comparative analyses revealed distinct performance characteristics: the CS/PVA-BA gel showed optimal elongation at break, low hydration (8.33 ± 0.57% water content), and potent antioxidant activity (DPPH: 73.59 ± 0.1%; ABTS: 77.47 ± 0.1%), whereas the S/PVA-BA gel demonstrated superior tensile strength and pH-responsive sensitivity. Structural characterization via FT-IR and SEM confirmed molecular compatibility between BA and polymeric matrices, with anthocyanins enhancing intermolecular hydrogen bonding. Applied to chilled beef (4 °C) freshness monitoring, the CS/PVA-BA gel exhibited color transformations from magenta-red (initial spoilage at 48 h: TVB-N > 15 mg/100 g, TVC > 4.0 lg CFU/g) to bluish-gray (advanced spoilage by day 6), correlating with proteolytic degradation metrics. These findings established a multifunctional platform for real-time food quality assessment through anthocyanin-mediated color changes in the composite gels, coupled with preservation activity, highlighting their significant potential as intelligent active packaging in the food industry. Full article

The paper presents changes in the color and acidity of beech wood with false heartwood in the process of pressure steaming at the temperature interval t = 105 °C and 125 °C during τ = 6 to 24 h. The light white-gray color of sapwood with a yellow tint changes to pale pink and red-brown to brown-red color during the steaming process. The color of beech wood with false heartwood changed to brown-gray color shades during 24 h of steaming with saturated water steam. From the measured data, as well as the visual evaluation of the color of the wood, I can conclude that, in the process of steaming beech wood with false heartwood, we can achieve color unification between false heartwood and sapwood in mode at temperature t = 105 °C for time τ = 18 h and in mode at temperature t = 125 °C for time τ = 12 h. Due to the influence of hemicellulose hydrolysis, the acidity of beech wood changes in the process of steaming. The decrease in acidity of beech wood in the temperature interval t = 105–125 °C and time τ = 6–24 h is in the range of values pH_sapwood = 5.2 to 3.6 and pH_{false heartwood} = 5.0 to 3.9. The relationship between the total color difference ∆E and the acidity change in beech sapwood and false heartwood is expressed by a second-degree polynomial function. The above mathematical relations represent a useful tool for evaluating the achieved color shade before further technological processing. Full article

This study presents a novel approach for the detection and severity assessment of pest-induced damage in maize plants, focusing on the Zyginidia pullula pest. A newly developed dataset is utilized, where maize plant images are initially classified into two primary categories: healthy and infected. Subsequently, infected samples are categorized into three distinct severity levels: low, medium, and high. Both traditional and deep learning-based feature extraction techniques are employed to achieve this. Specifically, hand-crafted feature extraction methods, including Gabor filters, Gray Level Co-occurrence Matrix, and Hue-Saturation-Value color space, are combined with CNN-based models such as ResNet-50, DenseNet-201, and EfficientNet-B2. The maize images undergo preprocessing and segmentation using Contrast Limited Adaptive Histogram Equalization and U2Net, respectively. Extracted features are then fused and subjected to Principal Component Analysis for dimensionality reduction. The classification task is performed using Support Vector Machines, Random Forest, and Artificial Neural Networks, ensuring robust and accurate detection. The experimental results demonstrate that the proposed hybrid approach outperforms individual feature extraction methods, achieving a classification accuracy of up to 92.55%. Furthermore, integrating multiple feature representations significantly enhances the model’s ability to differentiate between varying levels of pest damage. Unlike previous studies that primarily focus on plant disease detection, this research uniquely addresses the quantification of pest-induced damage, offering a valuable tool for precision agriculture. The findings of this study contribute to the development of automated, scalable, and efficient pest monitoring systems, which are crucial for minimizing yield losses and improving agricultural sustainability. Full article

In the process of mosaicking regional synthetic aperture radar (SAR) intensity images, multiple images with significant brightness anomalies can cause a considerable number of pixels to exceed the grayscale quantization range. Applying traditional color harmonization methods increases this issue, causing a loss of brightness information. We propose a multi-objective gray consistency correction method designed explicitly for mosaicking regional SAR intensity images with brightness anomalies to address this. We constructed a two-objective optimization model to ensure regional image gray consistency and mitigate brightness information loss. The truncation values of brightness anomaly images were selected as decision variables, maximizing the overall gray consistency of overlapping image pairs and minimizing the number of pixels with grayscale values that were out of bounds as the objective functions. To synchronously solve the truncation values of brightness anomaly images and linear stretch parameters of all images, a hybrid framework that combines the non-dominated sorting genetic algorithm II (NSGA-II) with the quadratic programming (QP) algorithm was proposed. Two large-area experimental results show that the proposed method achieves a balanced optimization between gray consistency and brightness information loss for regional SAR intensity image mosaicking. Compared with the traditional method, our method reduces brightness information loss by 99.552–99.647% and 99.973–99.969%, respectively, while maintaining better peak signal-to-noise ratio performance. Full article

This paper reports the first evidence of the presence of the mineral tremolite asbestos in Roman building materials from the Micia archaeological site (Romania), thus contributing to the understanding of the implications of ancient building materials. The Micia archaeological site includes both a fort and a civilian Roman military settlement that was inhabited by both civilians and soldiers from various Roman troops. Over time, since the late 2nd century AD, the settlement has undergone significant reconstruction, especially after some fires. Tremolite asbestos is a non-flammable mineral that, due to its fibrous properties, was used in the past in building materials, although it poses health risks when inhaled. To highlight it, several advanced and highly sensitive scientific techniques are used in this work to discover the presence of tremolite asbestos and to examine its structure, composition, and morphology inside the investigated samples. Tremolite asbestos is typically white to gray or greenish in color, characterized by thin, needle-like fibers that can easily become airborne and inhaled. It is a crystalline mineral that usually forms long, straight, sharp fibers. Under high magnification in optical microscopy or in scanning electron microscope images, correlated with other performant analytical techniques (XRD, WDXRF, FTIR, Raman, BET, TGA), tremolite asbestos appears as elongated, slender fibers—often bundled or intertwined—with smooth or slightly striated surfaces. Full article