Search Results (69)

The Goddess Temple at Niuheliang, located in Chaoyang City, Liaoning Province, is the earliest known temple excavated in China, offering profound insights into Neolithic religious architecture. Built during the Neolithic era, this sacred site reflects a deliberate integration of geographical features and early spiritual beliefs. The temple demonstrates a mythologically inspired architectural landscape, shaped by the local terrain and animal symbolism. Its design principles are evident in three main aspects. First, the alignment of the temple along the central axis of Niuheliang Mountain and its bird-shaped architecture—resembling an eagle and an owl—may embody the belief in sacred birds as intermediaries between humans and deities. Second, the goddess head within the temple mirrors the contours of Bear-Headed Mountain (Xiongshoushan 熊首山), suggesting a deliberate visual alignment between the goddess image and the form of the mountain. Third, the bear-shaped clay sculpture inside the temple conceptually links to Bear-Headed Mountain, potentially reflecting a widespread belief in the Celestial Bear (Tianxiong 天熊). This fusion of topography and myth exemplifies a distinctive approach to constructing sacred space in early Chinese religious culture, where the natural environment was not merely a backdrop but an active medium for expressing cosmological ideas. The Niuheliang Goddess Temple thus stands as a purposefully created mythological world, revealing the ancestors’ complex and sophisticated engagement with the natural landscape and spiritual beliefs. Full article

Background: Cardiovascular magnetic resonance (CMR) is the clinical gold standard for assessing cardiac anatomy and function. However, the manual segmentation of cardiac structures and myocardial infarction (MI) is time-consuming, prone to inter-observer variability, and often depends on contrast-enhanced imaging. Although deep learning (DL) has enabled substantial automation, challenges remain in generalizability, particularly for MI detection from non-contrast cine CMR. Objective: This study proposes a comprehensive DL-based framework for automatic segmentation of cardiac structures and myocardial infarction using contrast-free cine CMR. Methods: The framework integrates multiple convolutional neural network (CNN) architectures for cardiac structure segmentation with an attention-based deep learning model for MI localization. Post-processing refinement using stacked autoencoders and active contour modeling is applied to improve anatomical consistency. Segmentation performance is evaluated using overlap-based and boundary-based metrics, including the Dice Similarity Coefficient (DSC), Mean Contour Distance (MCD), and Hausdorff Distance (HD). Results: The best-performing model achieved Dice scores of 0.93 ± 0.05 for the left ventricular (LV) cavity, 0.89 ± 0.04 for the LV myocardium, and 0.91 ± 0.06 for the right ventricular (RV) cavity, with consistently low boundary errors across all structures. Myocardial infarction segmentation achieved a Dice score of 0.80 ± 0.02 with high recall, demonstrating reliable infarct localization without the use of contrast agents. Conclusions: By enabling accurate cardiac structure and myocardial infarction segmentation from contrast-free cine CMR, the proposed framework supports broader clinical applicability, particularly for patients with contraindications to gadolinium-based contrast agents and in emergency or resource-limited settings. This approach facilitates scalable, contrast-independent cardiac assessment. Full article

Current image segmentation methods often suffer from issues such as low accuracy, slow processing speed, and inadequate robustness when dealing with images with inhomogeneous noise and intensity. To resolve these issues, we propose a fast image segmentation algorithm based on a grayscale morphological edge differential fitting model. By utilizing morphological erosion and dilation operations, our model matches the differential image intensity inside and outside the contour. The grayscale morphological operator extracts local image information, which can effectively segment images with intensity inhomogeneity. Since the edge differential fitting function is replaced by the image grayscale morphology, it reduces the need for updates during level set evolution, thereby lowering the CPU runtime and complexity. Experimental results indicate that our model demonstrates fair robustness to noise interference and initial contours. Compared with active contour models (ACMs) and deep learning methods, our model exhibits superior segmentation accuracy while remaining robust to initial contours. Full article

This study proposes a clinically driven hybrid segmentation framework for dense breast tissue analysis in mammographic images, addressing persistent challenges associated with intensity inhomogeneity, low-contrast, and complex tumor morphology. The framework integrates Multiplicative Intrinsic Component Optimization (MICO_2D) for bias field correction, followed by a distance-regularized multiphase Vese–Chan level-set model for coarse global tumor segmentation. To achieve precise boundary delineation, a localized refinement stage is employed using Localized Active Contours (LAC) with Local Image Fitting (LIF) energy, supported by Gaussian regularization to ensure smooth and coherent boundaries in regions with ambiguous tissue transitions. Building upon the refined semantic tumor mask, the framework further incorporates a panoptic-style tumor instance segmentation stage, enabling the decomposition of connected tumor regions into distinct anatomical instances, which were evaluated on both MIAS and INBreast mammography datasets to demonstrate generalizability. This extension facilitates detailed structural analysis of tumor multiplicity and spatial organization, enhancing interpretability beyond conventional pixel wise segmentation. Experiments conducted on Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) mammographic views demonstrate competitive performance relative to baseline U-Net and advanced deep learning fusion architectures, including multi-scale and multi-view networks, while offering improved interpretability and robustness. Quantitative evaluation using overlap-related metrics shows strong spatial agreement between predicted and reference segmentations, with per-image Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) distributions reported to ensure reproducibility. Descriptive per-image analysis, supported by bootstrap-based confidence intervals and paired comparisons, indicates consistent performance improvements across images. Robustness analysis under realistic perturbations, including noise, contrast degradation, blur, and rotation, demonstrates stable performance across varying imaging conditions. Furthermore, feature space visualizations using t-SNE and UMAP reveal clear separability between cancerous and non-cancerous tissue regions, highlighting the discriminative capability of the proposed framework. Overall, the results demonstrate the effectiveness, robustness, and clinical motivation of this hybrid panoptic framework for comprehensive dense breast tumor analysis in mammography, while emphasizing reproducibility and conservative statistical assessment. Full article

There are several drawbacks in the existing localization (active contour) models. Some models have poor capability of handling uneven illuminations and low contrasts, others are sensitive to initial condition, and there are still some models which can not converge to the object boundary stably. In this paper, following the routes of Localizing Region-Based Active Contours (LRBAC) which is an important localization method, we propose a new localized active contour model. By altering the underlying construction logic, our proposed algorithm overcomes the problem of LRBAC with respect to poor convergence stability. Compared with some state-of-the-art localization models, our new algorithm is more similar to an edge-based one and therefore performs better when handling uneven illuminations and low contrasts. Moreover, combining the features of the region-based and the edge-based active contours, we propose, for our algorithm, a simple approach to dynamically control the localization size. This dynamical method makes our algorithm more robust to the initial condition. Detailed theoretical analysis and comparison are presented to clarify the features of our proposed algorithm. Experimental results on real-image segmentation underline the effectiveness of our proposed algorithm. Full article

Accurate displacement field measurement by holographic interferometry requires robust analysis of high-density fringe patterns, which is hindered by speckle noise inherent in any interferogram, no matter how perfect. Conventional skeletonization methods, such as edge detection algorithms and active contour models, often fail under these conditions, producing fragmented and unreliable fringe contours. This paper presents a novel skeletonization procedure that simultaneously addresses three fundamental challenges: (1) topology preservation—by representing the fringe family within a physics-informed, finite-dimensional parametric subspace (e.g., Fourier-based contours), ensuring global smoothness, connectivity, and correct nesting of each fringe; (2) extreme noise robustness—through a robust strip integration functional that replaces noisy point sampling with Gaussian-weighted intensity averaging across a narrow strip, effectively suppressing speckle while yielding a smooth objective function suitable for gradient-based optimization; and (3) sub-pixel accuracy without phase extraction—leveraging continuous bicubic interpolation within a recursive quasi-optimization framework that exploits fringe similarity for precise and stable contour localization. The method’s performance is quantitatively validated on synthetic interferograms with controlled noise, demonstrating significantly lower error compared to baseline techniques. Practical utility is confirmed by successful processing of a real interferogram of a bent plate containing over 100 fringes, enabling precise displacement field reconstruction that closely matches independent theoretical modeling. The proposed procedure provides a reliable tool for processing challenging interferograms where traditional methods fail to deliver satisfactory results. Full article

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes. Full article

Steep olive orchards in northwest Syria are experiencing severe land degradation as a result of unsustainable uphill–downhill tillage, which accelerates erosion and reduces productivity. To address this problem, three tillage systems, no-till natural vegetation strips (NVSs), contour tillage, and uphill–downhill tillage, were evaluated at two research sites, Yakhour and Tel-Hadya, NW Syria. The adoption of no-till NVSs significantly increased soil organic matter (SOM) at both sites, outperforming uphill–downhill tillage. While contour tillage resulted in lower SOM levels than NVSs, it still performed better than the conventional uphill–downhill practice. Contour soil flux (CSF) was lower in Yakhour, where mule-drawn tillage on steep slopes (31–35%) was practiced, compared to higher CSF values in Tel-Hadya, where tractor tillage was applied on gentler slopes (11–13%), which highlights the influence of slope steepness on soil fluxes. Over four years, net soil flux (NSF) indicated greater soil loss under tractor tillage, confirming that mule-drawn tillage is less disruptive. Olive trees with no-till NVSs benefited from protected root systems, improved soil structure through SOM accumulation, reduced erosion risk, and improved surface runoff buffering, which resulted in increased water infiltration and soil water retention. This study was carried out using a participatory technology development (PTD) framework, which guided the entire research process, from diagnosing problems to co-designing, field testing, and refining soil conservation practices. In Yakhour, farmers actively identified the challenges of degradation. They collaboratively chose no-till natural vegetation strips (NVSs) and contour tillage as key interventions, valuing NVSs for their ability to conserve moisture, suppress weeds and pests, and increase olive productivity. The farmer–scientist co-learning network positioned PTD not only as an outreach tool but also as a core research method, enabling locally relevant and scalable strategies to restore soil functions and combat land degradation in northwest Syria’s hilly olive orchards. Full article

Precise segmentation of flotation froths is a critical bottleneck to achieving intelligent perception and optimal control of process operations. Traditional convolutional neural networks (CNNs) are inherently limited by local receptive fields, making it challenging to accurately segment adhesive and multi-scale froths. To address this fundamental issue, this paper proposes a deep segmentation network with integrated global context awareness, termed GC-FSegNet, which establishes a new paradigm capable of jointly modeling macro-level structures and micro-level details. The proposed GC-FSegNet innovatively integrates the Global Context Network (GCNet) module into both the encoder and decoder of a Nested U-Net architecture. The GCNet captures long-range dependencies between froths, enabling macro-level modeling of clustered foam structures, while the Nested U-Net preserves high-resolution boundary details. Through their synergistic interaction, the model achieves simultaneous and efficient representation of both global contours and local details of froth images. Furthermore, the Mish activation function is employed to enhance the learning of weak boundary features, and a combined Dice and Binary Cross-Entropy (BCE) loss function is designed to optimize boundary segmentation accuracy. Experimental results on a self-constructed copper–lead flotation froth dataset demonstrate that GC-FSegNet achieves an mDice of 0.9443, mIoU of 0.8945, mRecall of 0.9866, and mPrecision of 0.9705, significantly outperforming mainstream models such as U-Net and DeepLabV3+. This study not only provides a reliable technical solution for high-adhesion froth segmentation but, more importantly, introduces a promising “global–local collaborative modeling” framework that can be extended to a wide range of complex industrial image segmentation scenarios. Full article

High-voltage bushings are critical insulation components, yet conventional PRPD-based severity assessment methods that rely on global pattern morphologies such as “rabbit ears” and “tortoise shell” remain coarse, lack local sensitivity, and fail to track continuous degradation. This paper proposes a dynamic severity assessment method that shifts the focus from global contours to dense partial discharge (PD) clusters, defined as high-density aggregations of PD pulses in specific phase–magnitude regions of PRPD patterns. Each dense cluster is treated as the statistical projection of a physical discharge channel, and the evolution of its number, intensity, location, and shape provides a fine-scale description of defect development. A multi-level relative density and morphological image processing algorithm is used to extract dense clusters directly from PRPD histograms, followed by a 20-dimensional feature set and a five-index system describing discharge activity, development speed, complexity, instability, and evolution trend. A fuzzy comprehensive evaluation model further converts these indices into three severity levels with confidence measures. Long-term degradation tests on defective bushings demonstrate that the proposed method captures key turning points from dispersed multi-cluster patterns to a single dominant cluster and yields a stable, stage-consistent severity evaluation, offering a more sensitive and physically interpretable tool for condition monitoring and early warning of HV bushings. The method achieved a high evaluation confidence (average 60.1%), which rose to 100% at the critical failure stage. It successfully identified three distinct degradation stages (stable, accelerated, and critical) across the 49 test intervals. A quantitative comparison demonstrated significant advantages: 8.3% improvement in early warning (4 windows earlier than IEC 60270), 50.6% higher monotonicity, 125.2% better stability, and 45.9% wider dynamic range, while maintaining physical interpretability and requiring no training data. Full article

To address the failure of unmanned aerial vehicle (UAV) target tracking caused by occlusion and limited field of view in dense low-altitude obstacle environments, this paper proposes a novel framework integrating occlusion-aware modeling and multi-UAV collaboration. A lightweight tracking model based on the Mamba backbone is developed, incorporating a Dilated Wavelet Receptive Field Enhancement Module (DWRFEM) to fuse multi-scale contextual features, significantly mitigating contour fragmentation and feature degradation under severe occlusion. A dual-branch feature optimization architecture is designed, combining the Distilled Tanh Activation with Context (DiTAC) activation function and Kolmogorov–Arnold Network (KAN) bottleneck layers to enhance discriminative feature representation. To overcome the limitations of single-UAV perception, a multi-UAV cooperative system is established. Ray intersection is employed to reduce localization uncertainty, while spherical sampling viewpoints are dynamically generated based on obstacle density. Safe trajectory planning is achieved using a Crested Porcupine Optimizer (CPO). Experiments on the Multi-Drone Multi-Target Tracking (MDMT) dataset demonstrate that the model achieves 84.1% average precision (AP) at 95 Frames Per Second (FPS), striking a favorable balance between speed and accuracy, making it suitable for edge deployment. Field tests with three collaborative UAVs show sustained target coverage in complex environments, outperforming traditional single-UAV approaches. This study provides a systematic solution for robust tracking in challenging low-altitude scenarios. Full article

This study presents the development and evaluation of an automated ultraviolet-C irradiation system for maize seed treatment, emphasizing disinfection performance, environmental control, and vision-based monitoring. The system features dual 8-watt ultraviolet-C lamps, sensors for temperature and humidity, and an air extraction unit to regulate the microclimate of the chamber. Without air extraction, radiation stabilized within one minute, with internal temperatures increasing by 5.1 °C and humidity decreasing by 13.26% over 10 min. When activated, the extractor reduced heat build-up by 1.4 °C, minimized humidity fluctuations (4.6%), and removed odors, although it also attenuated the intensity of ultraviolet-C by up to 19.59%. A 10 min ultraviolet-C treatment significantly reduced the fungal infestation in maize seeds by 23.5–26.25% under both extraction conditions. Thermal imaging confirmed localized heating on seed surfaces, which stressed the importance of temperature regulation during exposure. Notable color changes ($\Delta E>2.3$) in treated seeds suggested radiation-induced pigment degradation. Ultraviolet-C intensity mapping revealed spatial non-uniformity, with measurements limited to a central axis, indicating the need for comprehensive spatial analysis. The integrated computer vision system successfully detected seed contours and color changes under high-contrast conditions, but underperformed under low-light or uneven illumination. These limitations highlight the need for improved image processing and consistent lighting to ensure accurate monitoring. Overall, the chamber shows strong potential as a non-chemical seed disinfection tool. Future research will focus on improving radiation uniformity, assessing effects on germination and plant growth, and advancing system calibration, safety mechanisms, and remote control capabilities. Full article

Accurate computer-aided cell detection in immunohistochemistry images of different tissues is essential for advancing digital pathology and enabling large-scale quantitative analysis. This paper presents a comprehensive comparison of six unsupervised segmentation methods against two supervised deep learning approaches for cell detection in immunohistochemistry images. The unsupervised methods are based on the continuity and similarity image properties, using techniques like clustering, active contours, graph cuts, superpixels, or edge detectors. The supervised techniques include the YOLO deep learning neural network and the U-Net architecture with heatmap-based localization for precise cell detection. All these methods were evaluated using leave-one-image-out cross-validation on the publicly available OIADB dataset, containing 40 oral tissue IHC images with over 40,000 manually annotated cells, assessed using precision, recall, and $F_1$-score metrics. The U-Net model achieved the highest performance for cell nuclei detection, an $F_1$-score of 75.3%, followed by YOLO with $F_1$ = 74.0%, while the unsupervised OralImmunoAnalyser algorithm achieved only $F_1$ = 46.4%. Although the two former are the best solutions for automatic pathological assessment in clinical environments, the latter could be useful for small research units without big computational resources. Full article

A layerwise laminate FE model capable of predicting the dynamic response of delaminated composite beams with piezoelectric actuators and sensors encompassing local non-linear contact and sliding at the delamination interfaces was formulated. The kinematic assumptions of the layerwise model enabled the representation of opening and sliding of delamination interfaces as generalized strains, thereby allowing the introduction of interfacial contact and sliding effects through constitutive relations at the interface. This realistic FE model, assisted by representative experiments, was used to study the time response of delaminated active sensory composite beams with predefined delamination extents. The time response was measured and simulated for narrowband actuation signals at two distinct frequency levels using a surface-bonded piezoceramic actuator, while signal acquisition was performed with a piezopolymer sensor. Four different composite specimens, each containing a different delamination size, were used for this study. Experimental results were directly compared with model predictions to evaluate the performance of the proposed analytical approach. Damage signatures were identified in both the signal amplitude and the time of flight, and the sensitivity to delamination size was examined. Finally, the distributions of axial and interlaminar stresses at various time snapshots of the transient analysis are presented, along with contour plots across the structure’s thickness, which illustrate the delamination location and wave propagation patterns. Full article

Water body detection in synthetic aperture radar (SAR) imagery plays a critical role in applications such as disaster response, water resource management, and environmental monitoring. However, it remains challenging due to complex background interference in SAR images. To address this issue, a bi-encoder and hybrid feature fuse network (BiEHFFNet) is proposed for achieving accurate water body detection. First, a bi-encoder structure based on ResNet and Swin Transformer is used to jointly extract local spatial details and global contextual information, enhancing feature representation in complex scenarios. Additionally, the convolutional block attention module (CBAM) is employed to suppress irrelevant information of the output features of each ResNet stage. Second, a cross-attention-based hybrid feature fusion (CABHFF) module is designed to interactively integrate local and global features through cross-attention, followed by channel attention to achieve effective hybrid feature fusion, thus improving the model’s ability to capture water structures. Third, a multi-scale content-aware upsampling (MSCAU) module is designed by integrating atrous spatial pyramid pooling (ASPP) with the Content-Aware ReAssembly of FEatures (CARAFE), aiming to enhance multi-scale contextual learning while alleviating feature distortion caused by upsampling. Finally, a composite loss function combining Dice loss and Active Contour loss is used to provide stronger boundary supervision. Experiments conducted on the ALOS PALSAR dataset demonstrate that the proposed BiEHFFNet outperforms existing methods across multiple evaluation metrics, achieving more accurate water body detection. Full article