Search Results (455)

This study investigates the influence of façade-design parameters on daylighting performance in hot arid climates, with a particular focus on Egypt. A total of nine façade configurations of a residential building were modeled and simulated using Autodesk Revit and Insight 360, varying three critical variables: glazing type (clear, blue, and dark), Window-to-Wall Ratio (WWR) of 15%, 50%, 75%, and indoor wall finish (light, moderate, dark) colors. These were compared to the Leadership in Energy and Environmental Design (LEED) daylighting quality thresholds. The results revealed that clear glazing paired with high WWR (75%) achieved the highest Spatial Daylight Autonomy (sDA), reaching up to 92% in living spaces. However, this also led to elevated Annual Sunlight Exposure (ASE), with peak values of 53%, exceeding the LEED discomfort threshold of 10%. Blue and dark glazing types successfully reduced ASE to as low as 0–13%, yet often resulted in underlit spaces, especially in private rooms such as bedrooms and bathrooms, with sDA values falling below 20%. A 50% WWR emerged as the optimal balance, providing consistent daylight distribution while maintaining ASE within acceptable limits (≤33%). Similarly, moderate color wall finishes delivered the most balanced lighting performance, enhancing sDA by up to 30% while controlling reflective glare. Statistical analysis using Pearson correlation revealed a strong positive relationship between sDA and ASE (r = 0.84) in highly glazed, clear glass scenarios. Sensitivity analysis further indicated that low WWR configurations of 15% were highly influenced by glazing and finishing types, leading to variability in daylight metrics reaching ±40%. The study concludes that moderate glazing (blue), medium WWR (50%), and moderate color indoor finishes provide the most robust daylighting performance across diverse room types. These findings support an evidence-based approach to façade design, promoting visual comfort, daylight quality, and sustainable building practices. Full article

Deep space Color Remote Sensing Images (DCRSIs) are of great significance in reconstructing the three-dimensional appearance of celestial bodies. Among them, deep space color restoration, as a means to ensure the authenticity of deep space image colors, has significant research value. The existing deep space color restoration methods have gradually evolved into a joint restoration mode that integrates color images and spectrometers to overcome the limitations of on-orbit calibration plates; however, there is limited research on theoretical models for this type of method. Therefore, this article begins with the physical process of deep space color imaging, gradually establishes a color imaging spectral model, and proposes a new color restoration method for the color restoration of Mars remote sensing images. The experiment verifies that our proposed method can significantly reduce color deviation, achieving an average of 8.43 CIE DE 2000 color deviation units, a decrease of 2.63 (23.78%) compared to the least squares method. The color deviation decreased by 21.47 (71.81%) compared to before restoration. Hence, our method can improve the accuracy of color restoration of DCRSIs in space orbit. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

Fish coloration plays an important role in reproduction and camouflage, yet capturing color variation under field conditions remains challenging. We present a standardized, semi-automated protocol for measuring body coloration in the popular model fish threespine stickleback (Gasterosteus aculeatus). Individuals are photographed in a controlled light box within minutes of capture, and color is sampled from eight anatomically defined standard sites in human-perception-based CIELAB space. Analyses combine univariate color metrics, multivariate statistics, and the ΔE* perceptual difference index to detect subtle shifts in hue and brightness. Validation on pre-spawning fish shows the method reliably distinguishes males and females well before full breeding colors develop. Although it currently omits ultraviolet signals and fine-scale patterning, the approach scales efficiently to large sample sizes and varying lighting conditions, making it well suited for population-level surveys of camouflage dynamics, sexual dimorphism, and environmental influences on coloration in sticklebacks. Full article

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article

The human circadian rhythm is connected to the body’s endogenous clock and can influence people’s natural sleeping habits as well as a variety of other biological functions. According to research, various electric light sources in interior locations can disrupt the human circadian rhythm. Many psychological studies, on the other hand, reveal that different colors can have varied connections with and a variety of effects on people’s emotions. In this study, the effects of light source attributes and interior space paint color on human circadian rhythm were studied using 24 distinct computer simulations. Simulations were performed using the ALFA plugin for Rhinoceros 6 on an unfurnished bedroom 3D model at night. Results suggest that cooler hues, such as blue, appear to have an unfavorable effect on human circadian rhythm at night, especially when utilized in spaces that are used in the evening, which contradicts what psychologists and interior designers advocate in terms of the soothing mood and nature of the color. Furthermore, the effects of Correlated Color Temperature (CCT) and the intensity of a light source might be significant in minimizing melanopic lux to prevent melatonin suppression at night. These insights are significant for interior designers, architects, and lighting professionals aiming to create healthier living environments by carefully selecting lighting and color schemes that support circadian health. Incorporating these considerations into design practices can help mitigate adverse effects on sleep and overall well-being, ultimately contributing to improved occupant comfort and health. Full article

Image binarization algorithms reduce the original color space to only two values, black and white. They are an important preprocessing step in many computer vision applications. Image binarization is typically performed using a threshold value by classifying the pixels into two categories: lower and higher than the threshold. Global thresholding uses a single threshold value for the entire image, whereas local thresholding uses different values for the different pixels. Although slower and more complex than global thresholding, local thresholding can better classify pixels in noisy areas of an image by considering not only the pixel’s value, but also its surrounding neighborhood. This study introduces a local thresholding method that uses the results of several local thresholding algorithms and other image statistics to train a decision tree ensemble. Through cross-validation, we demonstrate that the model is robust and performs well on new data. We compare the results with state-of-the-art solutions and reveal significant improvements in the average F-measure for all DIBCO datasets, obtaining an F-measure of 95.8%, whereas the previous high score was 93.1%. The proposed solution significantly outperformed the previous state-of-the-art algorithms on the DIBCO 2019 dataset, obtaining an F-measure of 95.8%, whereas the previous high score was 73.8%. Full article

To address the challenges of unstable vibration signals, indistinct fault features, and difficulties in feature extraction during rolling bearing operation, this paper presents a novel fault diagnosis method based on the fusion of PSR-CRP and DenseNet. The Phase Space Reconstruction (PSR) method transforms one-dimensional bearing vibration data into a three-dimensional space. Euclidean distances between phase points are calculated and mapped into a Color Recurrence Plot (CRP) to represent the bearings’ operational state. This approach effectively reduces feature extraction ambiguity compared to RP, GAF, and MTF methods. Fault features are extracted and classified using DenseNet’s densely connected topology. Compared with CNN and ViT models, DenseNet improves diagnostic accuracy by reusing limited features across multiple dimensions. The training set accuracy was 99.82% and 99.90%, while the test set accuracy is 97.03% and 95.08% for the CWRU and JNU datasets under five-fold cross-validation; F1 scores were 0.9739 and 0.9537, respectively. This method achieves highly accurate diagnosis under conditions of non-smooth signals and inconspicuous fault characteristics and is applicable to fault diagnosis scenarios for precision components in aerospace, military systems, robotics, and related fields. Full article

Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality. Full article

This research was designed to explore science-fiction and fantasy (SFF) posters, specifically those related to films and television shows, from the perspective of their owners, examining their potential as sources of social and cultural significance and meaning. The research explored these in terms of the content of the poster, placement, media texts they reference, morals, behavior, identity, sense of self, well-being and self-expression. Data collection took place between 2020 and 2022 via an online survey (N = 273) and follow-up semi-structured interviews (N = 28) with adult science-fiction and fantasy film and television show poster owners. The significance and meaning of SFF posters were framed by two conceptual models: ‘The Three Significances’—esthetics, functionality, and significance (both spatial and personal)—and ‘The Big Three’—content, design, and color. Among these, content held the greatest significance for owners. Posters served as tools for self-expression, reflecting their owners’ identities, affinities, and convictions, while also reinforcing their connection to the media they reference. Posters helped to reinforce a sense of self and fan identity and evoke emotional responses, and the space in which they were displayed helped shape their meaning and significance. The paper sets out some suggestions for future research in this important topic. Full article

Image binarization is an important process in many computer-vision applications. This transforms the color space of the original image into black and white. Global thresholding is a quick and reliable way to achieve binarization, but it is inherently limited by image noise and uneven lighting. This paper introduces a global thresholding method that uses the results of classical global thresholding algorithms and other global image features to train a regression model via machine learning. We prove through nested cross-validation that the model can predict the best possible global threshold with an average F-measure of 90.86% and a confidence of 0.79%. We apply our approach to a popular computer vision problem, document image binarization, and compare popular metrics with the best possible values achievable through global thresholding and with the values obtained through the algorithms we used to train our model. Our results show a significant improvement over these classical global thresholding algorithms, achieving near-perfect scores on all the computed metrics. We also compared our results with state-of-the-art binarization algorithms and outperformed them on certain datasets. The global threshold obtained through our method closely approximates the ideal global threshold and could be used in a mixed local-global approach for better results. Full article

Heat release rate (HRR) is a key indicator for characterizing fire behavior, and it is conventionally measured under laboratory conditions. However, this measurement is limited in its widespread application to various fire conditions, due to its high cost, operational complexity, and lack of real-time predictive capability. Therefore, this study proposes an image-based HRR prediction model that uses deep learning and image processing techniques. The flame region in a fire video was segmented using the YOLO-YCbCr model, which integrates YCbCr color-space-based segmentation with YOLO object detection. For comparative analysis, the YOLO segmentation model was used. Furthermore, the fire diameter and flame height were determined from the spatial information of the segmented flame, and the HRR was predicted based on the correlation between flame size and HRR. The proposed models were applied to various experimental fire videos, and their prediction performances were quantitatively assessed. The results indicated that the proposed models accurately captured the HRR variations over time, and applying the average flame height calculation enhanced the prediction performance by reducing fluctuations in the predicted HRR. These findings demonstrate that the image-based HRR prediction model can be used to estimate real-time HRR values in diverse fire environments. Full article

Understanding how people perceive urban streetscapes is essential for enhancing the visual quality of the urban environment and optimizing street space design. While perceptions are shaped by the interplay of multiple visual elements, existing studies often isolate single semantic features, overlooking their combinations. This study proposes a Landscape Element Combination Extraction Method (SLECEM), which integrates the UniSal saliency detection model and semantic segmentation to identify landscape combinations that play a dominant role in human perceptions of streetscapes. Using street view images (SVIs) from the central area of Futian District, Shenzhen, China, we further construct a multi-dimensional feature–perception coupling analysis framework. The key findings are as follows: 1. Both low-level visual features (e.g., color, contrast, fractal dimension) and high-level semantic features (e.g., tree, sky, and building proportions) significantly influence streetscape perceptions, with strong nonlinear effects from the latter. 2. K-Means clustering of salient landscape element combinations reveals six distinct streetscape types and perception patterns. 3. Combinations of landscape features better reflect holistic human perception than single variables. 4. Tailored urban design strategies are proposed for different streetscape perception goals (e.g., beauty, safety, and liveliness). Overall, this study deepens the understanding of streetscape perception mechanisms and proposes a highly operational quantitative framework, offering systematic theoretical guidance and methodological tools to enhance the responsiveness and sustainability of urban streetscapes. Full article

This study proposes the PHD-YOLO model as a means to enhance the precision of passion fruit detection in intricate orchard settings. The model has been meticulously engineered to circumvent salient challenges, including branch and leaf occlusion, variances in illumination, and fruit overlap. This study introduces a pioneering partial convolution module (ParConv), which employs a channel grouping and independent processing strategy to mitigate computational complexity. The module under consideration has been demonstrated to enhance the efficacy of local feature extraction in dense fruit regions by integrating sub-group feature-independent convolution and channel concatenation mechanisms. Secondly, deep separable convolution (DWConv) is adopted to replace standard convolution. The proposed method involves decoupling spatial convolution and channel convolution, a strategy that enables the retention of multi-scale feature expression capabilities while achieving a substantial reduction in model computation. The integration of the HSV Attentional Fusion (HSVAF) module within the backbone network facilitates the fusion of HSV color space characteristics with an adaptive attention mechanism, thereby enhancing feature discriminability under dynamic lighting conditions. The experiment was conducted on a dataset of 1212 original images collected from a planting base in Yunnan, China, covering multiple periods and angles. The dataset was constructed using enhancement strategies, including rotation and noise injection, and contains 2910 samples. The experimental results demonstrate that the improved model achieves a detection accuracy of 95.4%, a recall rate of 85.0%, mAP@0.5 of 91.5%, and an F1 score of 90.0% on the test set, which are 0.7%, 3.5%, 1.3%, and 2. The model demonstrated a 4% increase in accuracy compared to the baseline model YOLOv11n, with a single-frame inference time of 0.6 milliseconds. The model exhibited significant robustness in scenarios with dense fruits, leaf occlusion, and backlighting, validating the synergistic enhancement of staged convolution optimization and hybrid attention mechanisms. This solution offers a means to automate the monitoring of orchards, achieving a balance between accuracy and real-time performance. Full article

Underwater image enhancement is a challenging task due to the unique optical properties of water, which often lead to color distortion, low contrast, and detail loss. At the present stage, the methods based on the CNN have the problem of insufficient global attention, and the methods based on Transformer generally have the problem of quadratic complexity. To address this challenge, we propose a dual-branch network architecture based on the W-shaped Mamba: UWMambaNet. Our method integrates the color contrast enhancement branch and the detail enhancement branch, and each branch is dedicated to improving specific aspects of underwater images. The color contrast enhancement branch utilizes the RGB and Lab color spaces and uses the Mamba block for advanced feature fusion to enhance color fidelity and contrast. The detail enhancement branch adopts a multi-scale feature extraction strategy to capture fine and contextual details through parallel convolutional paths. The Mamba module is added to the dual branches, and state-space modeling is used to capture the long-range dependencies and spatial relationships in the image data. This enables effective modeling of the complex interactions and light propagation effects inherent in the underwater environment. Experimental results show that our method significantly improves the visual quality of underwater images and is superior to existing technologies in terms of quantitative indicators and visualization effects; compared to the best candidate models on the UIEB and EUVP datasets, UWMambaNet improves UCIQE by 3.7% and 2.4%, respectively. Full article