Search Results (210)

This paper presents a deterministic system that combines textual semantic data from panoramic images with LiDAR point clouds in a mobile mapping setup. Urban scenes often include textual elements, such as signs and business names, that provide key details typically missing from LiDAR-based urban digital twins. The presented method uses deep learning-based OCR to extract text from street panoramas and then categorizes it into urban types using a rule-based classifier. Text regions are geometrically projected into the LiDAR environment by converting image coordinates into viewing rays that intersect LiDAR surfaces, such as facades. Data from multiple panoramas are merged with confidence-weighted spatial clustering to produce consistent semantic markers for urban features. Extracted business names enable text-based searches of the LiDAR point cloud, allowing facility location by category, keyword, or brand. Tests on datasets from European and U.S. cities support plausible facade-level localization and demonstrate the framework’s ability to enhance LiDAR point clouds with searchable semantic information. The main contribution is not a new standalone OCR or LiDAR-processing algorithm, but a deterministic multimodal integration framework that combines deep-learning OCR, geometric back-projection, and cross-view spatial fusion to convert street-level textual cues into reliable, queryable 3D semantic markers within mobile-mapping LiDAR data. Full article

Panoramic radiographs are widely used in dental practice due to their ability to provide a comprehensive view of the teeth, jaws, and surrounding anatomical structures in a single examination. However, automated interpretation remains challenging because multiple conditions may co-exist within a single image, class distributions are highly imbalanced, and several findings exhibit subtle radiographic characteristics. This study presents a deep learning framework for multi-label dental findings classification using panoramic radiographs from the publicly available VZRAD2 dataset. Following a label curation process, eleven clinically relevant classes were retained, including diseases, treatments, and anatomical structures. The proposed EfficientNet-B4-CBAM model integrates an EfficientNet-B4 backbone with a Convolutional Block Attention Module (CBAM) to enhance feature representation through channel and spatial attention. EfficientNet-B4 and ResNet50 were used as baseline models for comparison under a unified training protocol. The training pipeline incorporates data augmentation, weighted sampling to address class imbalance, AdamW optimization, and Binary Cross-Entropy with Logits loss for multi-label learning. On the validation set, the proposed model achieved the highest micro-F1 score of 0.8567, compared to 0.8424 for EfficientNet-B4 and 0.8469 for ResNet50. ROC analysis showed comparable separability across models, with micro-AUC values of 0.946 (EfficientNet-B4-CBAM), 0.947 (EfficientNet-B4), and 0.960 (ResNet50). Class-wise evaluation indicated strong performance for visually distinct findings such as impacted tooth, implant, filling, and root canal treatment, while anatomically diffuse or underrepresented classes remained more challenging. Grad-CAM visualizations suggest that the model focuses on clinically relevant regions, supporting interpretability. Overall, the results indicate that attention-enhanced convolutional models can provide effective and interpretable support for multi-label dental findings classification. However, the observed performance improvements are modest, and further validation on independent datasets, along with clinical evaluation, is required to confirm generalizability and real-world applicability. Full article

Image stitching is a computational technique designed to align and seamlessly fuse multiple overlapping images into a single panoramic image with an extended field of view. It plays a critical role in diverse domains, including mobile photography, autonomous navigation, and visual perception systems. However, most conventional image stitching pipelines implicitly assume that the input images have been pre-corrected for geometric distortions, particularly radial distortion inherent to wide-angle and fisheye lenses. This assumption often fails in practice, as many consumer-grade cameras lack built-in correction or calibration support. Consequently, applying standard image stitching methods to the uncorrected imagery frequently degrades feature correspondence reliability and introduces visible geometric misalignments and seam discontinuities in the final panorama. To overcome these limitations, this paper introduces a task-driven joint iterative optimization framework for image stitching that unifies unsupervised radial distortion correction, distortion-aware feature registration, and seam-aware blending within a single cohesive optimization objective. Specifically, lens distortion parameters are explicitly modeled as learnable variables and embedded into both the geometric registration and seam optimization sub-problems. An efficient closed-loop optimization strategy is then employed to jointly refine distortion parameters, homography estimates, and optimal seam paths in an alternating, mutually reinforcing manner. Implementation-wise, we first propose a calibration-free initial radial distortion estimation method which leverages intrinsic image gradients and epipolar consistency to provide physically plausible initialization for subsequent optimization. During iteration, distortion parameters are progressively refined by integrating robust geometric constraints derived from current feature matches (via RANSAC-based consensus filtering) with photometric consistency cues. Extensive experiments on multiple public benchmarks featuring pronounced radial distortion demonstrate that our method achieves superior stitching fidelity using metrics including PSNR and SSIM. It also confirms enhanced feature matching stability, which outperforms both distortion-agnostic approaches and two-stage pipelines that decouple distortion correction from registration. Furthermore, comprehensive ablation studies quantitatively and qualitatively validate the functional necessity and synergistic contribution of each core module, confirming the design rationale and effectiveness of the proposed joint optimization architecture. Full article

Urban green spaces provide essential ecosystem services, yet mismatches between subjective perceptions and objective assessments may constrain effective planning. This study examines the correspondence between perceived and measured ES across two contrasting urban green spaces in Shanghai: Century Park, a managed urban park, and Sanlin Green Space, a naturalistic urban forest. Objective ecosystem services (regulating, supporting, and cultural) were quantified using UAV-based biotope mapping and indicators including biophysical metrics (Net Primary Production, Water Retention, PM10 removal, and Land Surface Temperature), structural diversity indices (Shannon Diversity of land cover, vegetation, and tree structure), and visual–spatial proxies (Green View Index, Sky View Index, Water View Index, color metrics, and spatial openness). Subjective perceptions were derived from panoramic image-based questionnaires, with perception scores predicted using XGBoost and aggregated via SHapley Additive exPlanations (SHAP). Correlation analyses, spatial regression models, and partial least squares structural equation modeling were applied to explore relationships and pathways. Results show weak but significant positive associations in the urban park, whereas no overall correspondence was observed in the urban forest. Spatial mismatches were concentrated in biotopes with distinctive visual–ecological features and in fragmented areas. Green View Index is associated with higher perceptions in both sites, while the Sky View Index reduced perception in the forest context. These findings highlight strong context dependence in perceived–measured ecosystem service relationships and underscore the importance of integrating ecological structure and visual legibility in the design and management of the studied urban green spaces in Shanghai. Full article

Due to their advantages of being low-cost, lightweight and flexible, and having wide shooting coverage, UAVs have played an important role in situational awareness in the fields of disaster prevention and mitigation, urban planning and management, etc. In these applications, UAV aerial photography is limited by the field of view, and high-definition panoramic images of the complete target area cannot be obtained. Image mosaic technology is essential, but an image mosaic using only a single UAV cannot meet the high real-time requirements for situational awareness. In response to the above problems, this paper proposes a multi-UAV fast aerial image mosaic method based on key frames. First, the multi-UAV area coverage flight strategy is determined according to the size of the task area and the UAV flight parameters; then, the field of view of the pod, the flight speed, and the flight altitude are used to determine the key frame extraction time period during the UAV aerial photography process. The image matching-rate calculation method is designed and the key frames are extracted during the extraction time period, and the key frames are returned to the ground visual puzzle system; in the ground visual puzzle system, the improved Laplacian pyramid method is used to quickly fuse and stitch the key frames extracted by each UAV to obtain a panoramic stitched map. The experiment shows that the method can quickly obtain high-precision real-scene map information of the task area. Compared with the single-UAV method and the multi-UAV full video stream-splicing method, this method greatly reduces the consumption of computing power and the requirements of communication bandwidth and improves the efficiency and real-time performance of panoramic map acquisition. Full article

Unmanned aerial vehicle (UAV) image stitching aims to generate panoramic remote sensing images beyond the field of view of a single camera. However, there are still significant challenges in constructing panoramic images of a target area quickly and accurately, especially in terms of computationally intensive feature matching extraction and feature alignment accuracy, which are particularly sensitive to high-resolution and low-texture scenes. To address this problem, this study proposes an efficient image stitching method that incorporates dense depth kernelized feature extraction and geometric constraint optimization. The learning-based kernelized feature matching framework is adopted to achieve subpixel-level dense matching, which effectively overcomes the time-consuming and sparse matching deficiencies of traditional manual features (e.g., SIFT) in high-resolution images. Second, a two-layer geometrically constrained mismatching filtering strategy is designed, which significantly improves the alignment accuracy in low-texture and large-parallax scenarios. Finally, panoramic stitching is achieved through a hybrid strategy consisting of a single-responsive transform and max-intensity pixel blending strategy to realize panoramic stitching. Experimental results obtained on multiple datasets show that the proposed method achieves similar visual quality metrics (PSNR, SSIM, and LPIPS) while reducing the stitching time to just 17.5% of that of the baseline method. This makes it a practical solution for efficiently stitching large UAV images. Full article

People’s perception of the comfort level of street landscape elements is influenced by the built environment, and improving the quality of street landscape environment is of great significance for promoting the sustainable development of cities. This study focuses on 12 sample streets in Zibo City. After obtaining panoramic images of the area through the OSM platform, the FCN framework was used for semantic segmentation. A combination of subjective and objective methods was adopted, and eye tracking indicators were collected using the D-Lab wearable eye tracker. At the same time, a questionnaire quantitative analysis was conducted to systematically investigate the impact mechanism of the combination characteristics of street elements on comfort perception preferences. Research has found that there is a significant correlation between the perceived comfort preference of street scenes and GVI, and the increase in total gaze time towards green elements also shows a significant improvement in perceived comfort preference. After entering the street interface, observers show a high degree of priority attention to street view elements such as building facades and advertising facilities. As the gaze time on the sky (a street view element) increases, people’s perceived comfort evaluation shows a downward trend. There are significant differences in the structural characteristics of different streets, and their impact on improving comfort also varies to some extent. This study links the comfort perception of street landscape elements with sustainable urban development planning. By reasonably allocating landscape elements such as green visibility, basic roads, building interfaces, and signage facilities, it provides certain reference suggestions for the sustainable development of urban street space and human-centered urban construction. Full article

High-precision localization of Mars rovers is essential for safe path planning and efficient navigation toward scientific targets. As planetary rovers traverse the surface, their positional uncertainty accumulates, which can be corrected through global localization by registering rover images to orbital maps. To date, image-based solutions are widely adopted; however, substantial manual intervention is often required, which is time-consuming and limits the range of autonomous navigation. To address these challenges, we propose a two-stage localization framework, comprising the Mars cross-view few-shot training paradigm (MarsCVFP), Mars cross-view feature extraction network (MCVN) trained under MarsCVFP, and a robust template matching algorithm. Specifically, the MarsCVFP model can leverage implicit cross-view feature as guidance without relying on a large amount of high-precision location-level supervision and explicitly annotated, specific learning targets in the scene. MCVN can capture discriminative fine-grained features on the weakly textured and unstructured surface of Mars by constructing the multi-scale feature pyramid structure (MSFPS) and the feature interaction module (FIM). We validate our framework on 85 unit-planned sites and 20 panoramic sites, respectively, as traversed by the Zhurong rover. The experimental results demonstrate that our framework consistently outperforms both the traditional approaches and the representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains, achieving a localization success rate above 82% while maintaining a localization accuracy of better than 4 pixels, even under coarse prior positions uncertainties spanning 40 m × 40 m. Full article

Visual perception serves as a crucial interface connecting human psychology with the built environment. However, current studies on urban riverscapes often rely on static 2D imagery, failing to capture the spatial depth and immersive experience essential for ecological validity. Furthermore, the “black box” nature of traditional machine learning models hinders the understanding of how specific environmental features drive public perception. To address these gaps, this study proposes an innovative framework integrating high-fidelity 3D models, computer vision (CV), and interpretable artificial intelligence (XAI). Using the River Thames (London) and the River Seine (Paris) as diverse case studies, we constructed high-precision 3D digital twins to quantify 3D spatial metrics (e.g., Viewshed Area, H/W Ratio) and applied the SegFormer model to extract 2D visual elements (e.g., Green View Index) from water-based panoramic imagery. Subjective perception data were collected via immersive Virtual Reality (VR) experiments. Random Forest models combined with SHAP were employed to decode the non-linear driving mechanisms of perception. The results reveal three universal principles: (1) Sense of Affluence and Vibrancy are primarily driven by high building density and vertical enclosure, challenging the traditional preference for openness in waterfronts; (2) Scenic Beauty is determined by a synergy of high Green View Index and quality artificial interfaces, suggesting a preference for nature-culture integration; (3) Sense of Boredom is significantly positively correlated with Viewshed Area, indicating that empty prospects without visual foci lead to monotony. This study demonstrates the efficacy of integrating Digital Twins and XAI in revealing robust perception mechanisms across different urban contexts, providing a scientific, evidence-based tool for precision urban planning and riverside regeneration. Full article

Urban waterfront walkways are everyday public built environments where people commonly engage in slow walking, yet evidence remains limited that links what pedestrians see to immediate psychophysiological responses under controlled first-person dynamic exposure. To address this gap, we developed a fixed-speed, fixed-duration VR walk-through model using real-world 360° panoramic video and quantified scene visual composition via computer vision-based image analysis. Based on the visible shares of key components (greenery, water, sky, hardscape, and built structures), clips were grouped into four interpretable waterfront typologies: Vegetation-Enclosed, Built-Dominant, Hardscape-Plaza, and Blue-Open. Fifty healthy adults completed within-subject VR exposures to the four typologies (50 s per clip), while multimodal physiological signals and brief affect and landscape ratings were collected before and after exposure. The results showed that scenes with more water and vegetation coverage, along with expansive views, were associated with promoted autonomic nervous system calming responses, whereas scenes with fewer natural elements and higher built structure density were more likely to induce tension responses. Negative emotions decreased significantly across all four scene experiences, though artificial scenes concurrently exhibited emotional improvement alongside physiological tension. Overall, brief first-person dynamic VR exposure can yield immediate emotional benefits, and waterfront designs combining water proximity, abundant greenery, and expansive vistas may maximize short-term restorative potential, offering quantitative targets for health-supportive planning and retrofitting. Full article

Eye tracking can provide quantitative indices of visual exploration and cognitive processing during radiographic image interpretation. This study examined eye-movement patterns and pupil dynamics and their associations with task performance while fifth-year dental students interpreted a single mixed-dentition panoramic radiograph under free-viewing conditions. Task performance was defined as the number of correctly identified pre-specified items (three radiographic findings plus two interpretive items: dental age estimation and the presence/absence of congenital anomalies). Eye-movement patterns were classified into four groups: clockwise (R, 29.6%), counterclockwise (L, 44.4%), saccadic (S, 16.7%), and concentrated (C, 9.3%). Clockwise scan paths were associated with higher task scores and more globally distributed fixations than other patterns (p < 0.001). Linear mixed-effects modeling suggested that task scores increased up to 120 s of viewing time, whereas longer viewing times were not associated with further improvements. Furthermore, ordinal logistic regression analysis revealed that higher task scores were significantly associated with a smaller mean pupil area across the entire viewing time, combined with a larger pupil area specifically during fixations, suggesting more selective allocation of cognitive resources. These findings indicate associations between global scan structure, time allocation, pupil dynamics, and task performance in this single-image setting. Generalization to overall diagnostic competence or other radiographs requires replication using multiple panoramic images and a broader range of verified findings. Full article

We present a panoramic view of several scaling relations (ScRs) of galaxies of different morphology. The ScRs are obtained from the data of two large surveys (WINGS and MANGA). We analyze the distribution (parameterized by the percent over the total) of galaxies in each region of the diagnostic planes that are set up by means of suitable physical quantities. In addition to this, we discuss the origin of the differences observed in the ScRs between the two samples. Finally, we compare the observational data with the theoretical ones taken from two subsets of the Illustris large-scale simulations (TNG50 and TNG100), and we discuss how the comparison should be performed for a correct statistical answer. Full article

Low-cost RGB intraoral cameras are accessible alternatives to intraoral scanners; however, generating panoramic images is challenging due to narrow fields of view, textureless surfaces, and specular highlights. This study proposes a robust stitching framework and identifies optimal acquisition parameters to overcome these limitations. All experiments were conducted exclusively on a mandibular dental phantom model. Geometric consistency was further validated using repeated physical measurements of mandibular arch dimensions as ground-truth references. We employed a deep learning-based approach using SuperPoint and SuperGlue to extract and match features in texture-poor environments, enhanced by a central-reference stitching strategy to minimize cumulative drift errors. To validate the feasibility in a controlled setting, we conducted experiments on dental phantoms varying working distances (1.5–3.0 cm) and overlap ratios. The proposed method detected approximately 19–20 times more valid inliers than SIFT, significantly improving matching stability. Experimental results indicated that a working distance of 2.5 cm offers the optimal balance between stitching success rate and image detail for handheld operation, while a 1/3 overlap ratio yielded superior geometric integrity. This system demonstrates that robust 2D dental mapping is achievable with consumer-grade sensors when combined with advanced deep feature matching and optimized acquisition protocols. Full article

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified. Full article

This study curates and structures knowledge concerning Buddhist learning resources for tourism, presenting it through a virtual tour of Wat Phra Pathom Chedi Ratchaworamahawihan in Nakhon Pathom Province. Employing a mixed-methods approach that integrates both qualitative and quantitative methodologies, the research first establishes a structured knowledge base. This involves developing a comprehensive metadata schema for cataloging the temple’s diverse resources, including both sacred sites and artifacts, to enhance their searchability and accessibility. Subsequently, this knowledge is rendered into a virtual tour, which serves as an exemplary model of a Buddhist digital learning resource for tourism. The findings reveal the extensive diversity of resources within the temple. The developed virtual tour platform allows users an immersive exploration of the site via 360-degree panoramic views. This research presents significant implications for relevant agencies, offering a scalable model for the digital dissemination of cultural heritage. It is anticipated that this initiative will expand global access to and appreciation of the temple’s cultural value, thereby fostering international interest in visitation. Such engagement is poised to stimulate the local economy and bolster Thailand’s image as a premier cultural tourism destination. Full article