Search Results (650)

This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations. Full article

Multiple-resolution modeling (MRM) has emerged as a foundational paradigm in modern simulation, enabling the integration of models with varying levels of granularity to address complex and evolving operational demands. By supporting seamless transitions between high-resolution and low-resolution representations, MRM facilitates scalability and interoperability, particularly within distributed simulation environments such as military command and control systems. This paper provides a structured review and comparative analysis of prominent MRM methodologies, including multi-resolution entities (MRE), agent-based modeling (from a federation viewpoint), hybrid frameworks, and the novel MR mode, synchronizing resolution transitions with time advancement and interaction management. Each approach is evaluated across critical dimensions such as consistency, computational efficiency, flexibility, and integration with legacy systems. Emphasis is placed on the applicability of MRM in distributed military simulations, where it enables dynamic interplay between strategic-level planning and tactical-level execution, supporting real-time decision-making, mission rehearsal, and scenario-based training. The paper also explores emerging trends involving artificial intelligence (AI) and large language models (LLMs) as enablers for adaptive resolution management and automated model interoperability. Full article

Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives—front, back, and top-down—to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Full article

Cross-view image matching between UAV and satellite platforms is critical for geographic localization but remains challenging due to domain gaps caused by disparities in imaging sensors, viewpoints, and illumination conditions. To address these challenges, this paper proposes an Adaptive Threshold-guided Ring Partitioning Framework (ATRPF) for UAV–satellite cross-view image matching. Unlike conventional ring-based methods with fixed partitioning rules, ATRPF innovatively incorporates heatmap-guided adaptive thresholds and learnable hyperparameters to dynamically adjust ring-wise feature extraction regions, significantly enhancing cross-domain representation learning through context-aware adaptability. The framework synergizes three core components: brightness-aligned preprocessing to reduce illumination-induced domain shifts, hybrid loss functions to improve feature discriminability across domains, and keypoint-aware re-ranking to refine retrieval results by compensating for neural networks’ localization uncertainty. Comprehensive evaluations on the University-1652 benchmark demonstrate the framework’s superiority; it achieves 82.50% Recall@1 and 84.28% AP for UAV→Satellite geo-localization, along with 90.87% Recall@1 and 80.25% AP for Satellite→UAV navigation. These results validate the framework’s capability to bridge UAV–satellite domain gaps while maintaining robust matching precision under heterogeneous imaging conditions, providing a viable solution for practical applications such as UAV navigation in GNSS-denied environments. Full article

Civil structures carry significant service loads over long times but are prone to deterioration due to various natural impacts. Traditionally, these structures are inspected in situ by qualified engineers, a method that is high-cost, risky, time-consuming, and prone to error. Recently, researchers have explored innovative practices by using virtual reality (VR) technologies as inspection platforms. Despite such efforts, a critical question remains: can VR models accurately reflect real-world structural conditions? This study presents a comprehensive framework for assessing the visual fidelity of VR models for structural inspection. To make it viable, we first introduce a novel workflow that integrates UAV-based photogrammetry, computer graphics, and web-based VR editing to establish interactive VR user interfaces. We then propose a visual fidelity assessment methodology that quantitatively evaluates the accuracy of the VR models through image alignment, histogram matching, and pixel-level deviation mapping between rendered images from the VR models and UAV-captured images under matched viewpoints. The proposed frameworks are validated using two case studies: a historic stone arch bridge and a campus steel building. Overall, this study contributes to the growing body of knowledge on VR-based structural inspections, providing a foundation for our peers for their further research in this field. Full article

The Segment Anything Model 2 (SAM2) has achieved state-of-the-art performance in pixel-level object segmentation for both static and dynamic visual content. Its streaming memory architecture maintains spatial context across video sequences, yet struggles with long-term tracking due to its static inference framework. SAM 2’s fixed temporal window approach indiscriminately retains historical frames, failing to account for frame quality or dynamic motion patterns. This leads to error propagation and tracking instability in challenging scenarios involving fast-moving objects, partial occlusions, or crowded environments. To overcome these limitations, this paper proposes SAM2Plus, a zero-shot enhancement framework that integrates Kalman filter prediction, dynamic quality thresholds, and adaptive memory management. The Kalman filter models object motion using physical constraints to predict trajectories and dynamically refine segmentation states, mitigating positional drift during occlusions or velocity changes. Dynamic thresholds, combined with multi-criteria evaluation metrics (e.g., motion coherence, appearance consistency), prioritize high-quality frames while adaptively balancing confidence scores and temporal smoothness. This reduces ambiguities among similar objects in complex scenes. SAM2Plus further employs an optimized memory system that prunes outdated or low-confidence entries and retains temporally coherent context, ensuring constant computational resources even for infinitely long videos. Extensive experiments on two video object segmentation (VOS) benchmarks demonstrate SAM2Plus’s superiority over SAM 2. It achieves an average improvement of 1.0 in J&F metrics across all 24 direct comparisons, with gains exceeding 2.3 points on SA-V and LVOS datasets for long-term tracking. The method delivers real-time performance and strong generalization without fine-tuning or additional parameters, effectively addressing occlusion recovery and viewpoint changes. By unifying motion-aware physics-based prediction with spatial segmentation, SAM2Plus bridges the gap between static and dynamic reasoning, offering a scalable solution for real-world applications such as autonomous driving and surveillance systems. Full article

The current study investigates the chemical profiling, antioxidant activities, and enzyme inhibitory and cytotoxic potential of the water and methanolic extracts of different parts (flower, leaf, and bulb) of Muscari armeniacum. Chemical profiling was performed using UHPLC-MS/MS. At the same time, different in vitro assays were employed to support the results for antioxidant potential, such as DPPH, ABTS, FRAP, CUPRAC, metal chelation, and PBD, along with the measurement of total phenolic and flavonoid contents. Enzyme inhibition was investigated for cholinesterase (AChE and BChE), α-amylase, α-glucosidase, and tyrosinase enzymes. Additionally, the relative expression of NRF2, HMOX1, and YGS was evaluated by qPCR. LC-MS/MS analysis indicated the presence of some significant compounds, including apigenin, muscaroside, hyacinthacine A, B, and C, and luteolin. According to the results, the highest TPC and TFC were obtained with both extracts of the leaves, followed by the water extract (flower) and methanolic extract of the bulb. In contrast, the methanolic extract from the bulb exhibited the highest antioxidant potential using DPPH, ABTS, CUPRAC, and FRAP, followed by the extracts of leaves. In contrast, the leaf extracts had the highest values for the PBD assay and maximum chelation ability compared to other tested extracts. According to the enzyme inhibition studies, the methanolic extract from the bulb appeared to be the most potent inhibitor for all the tested enzymes, with the highest values obtained for AChE (1.96 ± 0.05), BChE (2.19 ± 0.33), α-amylase (0.56 ± 0.02), α-glucosidase (2.32 ± 0.01), and tyrosinase (57.19 ± 0.87). Interestingly, the water extract from the bulb did not inhibit most of the tested enzymes. The relative expression of NRF2 based on qPCR analysis was considerably greater in the flower methanol extract compared to the other extracts (p < 0.05). The relative expression of HMOX1 was stable in all the extracts, whereas YGS expression remained stable in all the treatments and had no statistical differences. The current results indicate that the components of M. armeniacum (leaves, flowers, and bulb) may be a useful source of natural bioactive compounds that are effective against oxidative stress-related conditions, including hyperglycemia, skin disorders, and neurodegenerative diseases. Complementary in silico approaches, including molecular docking, dynamics simulations, and transcription factor (TF) network analysis for NFE2L2, supported the experimental findings and suggested possible multi-target interactions for the selected compounds. Full article

The quality of children’s early childhood education (ECE) experiences significantly impacts their long-term outcomes and wellbeing. While extensive research has explored quality from the perspectives of adult stakeholders, including educators and authorities, there remains a paucity of studies prioritizing the viewpoint of children, the main beneficiaries of ECE. This study sought to address this gap by investigating children’s preschool experiences at an Australian inner-city preschool center. Using child-friendly interview techniques, researchers engaged 32 children aged 3–4 years in discussions about their likes, dislikes, and desired changes in their preschool settings. Open-ended questions such as “What do you love about preschool?” and “What do you think makes a good preschool?” were used to encourage reflection and storytelling. To complement verbal responses, children were invited to illustrate their thoughts through drawings, offering a visual dimension to their perspectives. Deductive thematic analysis identified eight themes within the dimensions of structural and process quality. The findings highlight the unique and insightful ways young children interpret their experiences, shedding light on aspects of preschool life they value most. By amplifying children’s voices, this study highlights the importance of integrating their perspectives into the design and evaluation of ECE environments, promoting practices that better align with their needs and support their wellbeing. Full article

Video-based person re-identification (re-identification) remains underused in real-world deployments, despite impressive benchmark performance. Most existing models rely on superficial correlations—such as clothing, background, or lighting—that fail to generalize across domains, viewpoints, and temporal variations. This study examines the emerging role of causal reasoning as a principled alternative to traditional correlation-based approaches in video-based re-identification. We provide a structured and critical analysis of methods that leverage structural causal models (SCMs), interventions, and counterfactual reasoning to isolate identity-specific features from confounding factors. This study is organized around a novel taxonomy of causal re-identification methods, spanning generative disentanglement, domain-invariant modeling, and causal transformers. We review current evaluation metrics and introduce causal-specific robustness measures. In addition, we assess the practical challenges—scalability, fairness, interpretability, and privacy—that must be addressed for real-world adoption. Finally, we identify open problems and outline future research directions that integrate causal modeling with efficient architectures and self-supervised learning. This study aims to establish a coherent foundation for causal video-based person re-identification and catalyze the next phase of research in this rapidly evolving domain. Full article

Aerial tracking is an important service for many Unmanned Aerial Vehicle (UAV) applications. Existing work has failed to provide robust solutions when handling target disappearance, viewpoint changes, and tracking drifts in practical scenarios with limited UAV resources. In this paper, we propose a closed-loop framework integrating three key components: (1) a lightweight adaptive detection with multi-scale feature extraction, (2) spatiotemporal motion modeling through Kalman-filter-based trajectory prediction, and (3) autonomous decision-making through composite scoring of detection confidence, appearance similarity, and motion consistency. By implementing dynamic detection-tracking coordination with quality-aware feature preservation, our system enables real-time operation through performance-adaptive frequency modulation. Evaluated on VOT-ST2019 and OTB100 benchmarks, the proposed method yields marked improvements over baseline trackers, achieving a 27.94% increase in Expected Average Overlap (EAO) and a 10.39% reduction in failure rates, while sustaining a frame rate of 23–95 FPS on edge hardware. The framework achieves rapid target reacquisition during prolonged occlusion scenarios through optimized protocols, outperforming conventional methods in sustained aerial surveillance tasks. Full article

Food security and environmental quality related to food production are global issues that need urgent solutions. Proteins are crucial for diets, and demand is growing for innovative and more environmentally sustainable sources of protein, like vegetables, microorganisms, and insects, and lab-grown food that can meet nutritional and environmental goals. This study analyzes a time series to assess the sustainability of different protein sources by evaluating their effects on emissions of greenhouse gases and the use of agricultural land while accounting for the carbon sink potential across the supply chain. The study also explores future trends in global protein sources, emphasizing shellfish as a key to achieving food security from both nutritional and environmental perspectives. By reviewing terrestrial livestock, farmed seafood, vegetal proteins, and alternative sources like insects and cultured cells, the study assesses sustainability, food security potential, and challenges from nutritional, environmental, and consumer viewpoints. We conclude that shellfish aquaculture, particularly oysters, mussels, clams, and scallops, has significant potential in enhancing food security, fostering sustainable protein consumption, reducing land use, and contributing to climate change mitigation by sequestering significant amounts of atmospheric carbon. Full article

Infrastructures of access networks that mostly exploit the optical fiber medium effectively utilizing wavelength division multiplexing techniques play a key role in advanced F5G fixed networks. The orbital angular momentum technique is highly promising for use within passive optical networks to further increase transmission capacities. So, the utilization of common network resources in wavelength and optical domains will be more important. The main purpose of this paper is to present an analysis of resource efficiencies for various allocation methods applied in the proposed OAM&WDM-PON architecture with a conventional point-to-multipoint topology. This contribution introduces novel static, dynamic and dynamic customized allocation methods for a proposed network design with the utilization of only passive optical splitters in remote nodes. These WDM and OAM channel allocation methods are oriented towards minimizing the number of working wavelengths and OAM channels that will be used for compliance with customers’ requests for data transmitting in the proposed point-to-multipoint OAM&WDM-PON architecture. For analyzing and evaluating the considered allocation methods, a simulation model related to the proposed P2MP OAM&WDM-PON design realized in the MATLAB (R2022A) programming environment is presented with acquired simulation results. Finally, resource efficiencies of the presented novel allocation methods are evaluated from the viewpoint of application in future OAM&WDM-PONs. Full article

This study compared the decomposition effects of different microbial agents added to rice straw to screen for efficient and stable microbial agents and achieve effective utilization of rice straw resources. Different microbial agents can accelerate the decomposition of rice straw. The E4/E6 value of rice straw added with the Bacillus subtilis agent was significantly lower than that of rice straw added with other microbial agents on the 30th day. The lignin degradation rates for the Bacillus subtilis agent and Trichoderma viride agent treatments were higher than those of the other treatments from the 5th to 30th days. After adding the Bacillus subtilis agent for 30 days, the degradation rates of hemicellulose and cellulose in rice straw were higher than others, reaching 33.62% and 41.31%, respectively. Through principal component analysis and grey relational analysis, it was determined that the C/N ratio, organic carbon, E4/E6 value, conductivity value, and pH value are important evaluation indicators for the maturity promotion effect. Using the membership function analysis method, it was found that the Bacillus subtilis agent had the best overall performance in straw decomposition. This research provides a new viewpoint for the efficient utilization of straw resources. Full article

Narrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection is heavily reliant on the cartographer’s subjective interpretation of the event. Even with fixed-angle settings, the task of ensuring that selected viewpoints align with the narrative theme remains challenging. To address this, an automated viewpoint selection method constrained by narrative relevance and visual information is proposed. Narrative relevance is determined by calculating spatial distances between each element and the thematic element within the scene. Visual information is quantified by assessing the visual salience of elements as the ratio of their projected area on the view window to their total area. Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. Experimental results indicate that, compared to the maximum projected area method and fixed-value method, a higher viewpoint fitness is achieved by this approach. The narrative views generated by this method were positively recognized by approximately two-thirds of invited professionals. This process aligns effectively with narrative visualization needs, enhances 3D narrative map creation efficiency, and offers a robust strategy for viewpoint selection in 3D scene-based narrative mapping. Full article

In 3D virtual environments, effective information presentation relies on clear label layouts to annotate complex objects. As label density increases, structured and adaptive layouts become critical to prevent visual clutter and maintain clarity. However, existing label layout methods often overlook the issue of double vision between labels caused by binocular disparity. Additionally, discrete update strategies in 3D label layouts struggle to maintain layout quality during viewpoint changes due to their limited real-time adaptability. To address these issues, this paper presents a binocular 3D dynamic label layout method based on a continuous updating strategy within a force-based framework, generating real-time binocular layouts to minimize visual confusion. Computational evaluation and a user study demonstrate that the proposed method significantly reduces double vision, as quantified by a reduction in double vision degree of up to 15.74%, along with label overlap and leader line crossings. The results further indicate that our approach surpasses existing methods in clarity, aesthetics, adaptability, and user satisfaction, while reducing label reading time and alleviating users’ physical and effort demands, particularly in high-density label environments. Full article