Search Results (153)

In visual object recognition problems, low light exposure and low-quality images present significant challenges in navigation, surveillance, and image retrieval applications, where reliable feature detection is critical. Although recent deep learning–based image enhancement methods improve visual quality in the pixel domain, these improvements often do not translate to downstream machine vision performance, as important local gradient structures required for stable key point detection are frequently suppressed. In this work, we propose IllumiSIFT, a task-driven dark image enhancement framework that focuses on preserving Scale-Invariant Feature Transform (SIFT) key points by directly learning the Difference-of-Gaussian (DoG) pyramid from low-light image inputs. Unlike conventional pixel-level recovery approaches, the proposed method employs a cascaded residual learning architecture to predict Gaussian-blurred representations at multiple scales, enabling the generation of enhanced DoG images that are inherently aligned with the SIFT detection process. Extensive experiments conducted on the CDVS, Oxford Buildings, and Paris datasets demonstrate that the proposed approach consistently outperforms state-of-the-art enhancement methods in downstream SIFT matching performance under severe low-light conditions. These results confirm that gradient-domain, task-aligned enhancement provides a more effective and practical solution for recognition-centric low-light imaging applications. Full article

The precise identification of minute, high-velocity entities within unconstrained visual fields represents a significant hurdle in computational perception. This difficulty primarily arises from the geometric degradation stemming from scale volatility, motion-induced asymmetry, and heterogeneous background clutter. To mitigate the critical deficit of high-fidelity benchmarks for dynamic micro-targets, we present Soccer-Wild. This comprehensive dataset is characterized by the extreme visual complexity of microscopic objects in diverse ecological settings. Built upon this empirical foundation, we introduce GOAL (Global Object Alignment for Localization). This novel computational paradigm is designed to enhance the weak features of tiny targets by integrating frequency-domain filtering, dynamic feature routing, and entropy-guided probabilistic modeling. The GOAL framework rigorously preserves spatial-structural equilibrium and information fidelity through three synergetic mechanisms: (1) Spectral Purification: We implement a Frequency-aware Spectral Gating approach that operates in the Fourier manifold, suppressing stochastic noise to accentuate the spectral signatures of the targets; (2) Geometric Adaptation: A Multi-Granularity Mixture of Experts (MG-MoE) is formulated with heterogeneous receptive fields to dynamically rectify anisotropic distortions caused by kinetic blurring. This adaptive routing ensures cross-state representation consistency; (3) Information Recovery: We propose Information-Guided Gaussian Distribution Estimation (IGDE), which utilizes information entropy to conceptualize target coordinates as radially symmetric probability densities. This facilitates the implicit recovery of latent signals typically discarded by rigid deterministic regression. Empirical validations on the Soccer-Wild and VisDrone2019 benchmarks reveal that the proposed methodology yields substantial gains in precision. Specifically, our model achieves 40.0% and 40.4% AP (Average Precision), respectively, establishing a new state-of-the-art for localizing highly dynamic, micro-scale objects. Full article

In aerial sensor systems, detecting helicopters against diverse backgrounds remains challenging due to environmental camouflage. This paper proposes an end-to-end framework for generating adaptive camouflage patterns to evade YOLO-based object detection. Starting with synthetic sensor imagery (background + transparent helicopter overlay), we employ a fine-tuned YOLOv8m for precise VTOL mask extraction, followed by KMeans clustering with Gaussian blur for dominant color extraction from the background. These colors guide Stable Diffusion inpainting to synthesize full-screen camouflage textures, which are then masked and overlapped onto the helicopter region. Evaluated on a 920-image dataset across multiple backgrounds, our method achieves a 97.6% reduction in mAP@0.5 (from 0.8175 to 0.0196) on 751 camouflaged images against a fine-tuned YOLOv8m model, with recall dropping by 95.9%. Even against a helicopter-specialized Defence model, mAP@0.5 drops by 89.6% (from 0.1178 to 0.0123). Ablation studies confirm the synergy of YOLO masking and color-guided inpainting. This sensor-fusion approach enhances stealth in unmanned aerial surveillance, with implications for civilian aviation safety. Full article

Background: Cone-beam computed tomography (CBCT) is increasingly applied for the assessment of periodontal bone levels. However, its measurement reliability and consistency depend strongly on image quality parameters such as voxel size, noise, and reconstruction sharpness. With the growing use of CBCT datasets for artificial intelligence (AI)-based diagnostics, it is essential to understand how image degradation conditions affect examiner-derived measurement outcomes and the reliability of reference data used for AI training. Methods: An anonymized CBCT dataset containing one periodontally healthy tooth (31) and one tooth with pronounced periodontal bone loss (41) was analyzed. The original DICOM data were systematically degraded using controlled voxel enlargement (double and triple voxel size) and simulated image blur (Gaussian and median filtering). Six dentists (n = 6) independently performed standardized linear bone-level measurements, with three repeated measurements per tooth and image condition. Data were analyzed using the Shapiro–Wilk test for normality assessment, the Kruskal–Wallis H test for group comparisons, Bonferroni-adjusted Mann–Whitney U tests for post hoc pairwise comparisons, and intraclass correlation coefficients (ICC (2,1)) for inter-examiner reliability assessment. Results: A total of 180 measurements were evaluated. Image degradation conditions were associated with statistically significant differences in bone-level measurements for both teeth (tooth 31: p = 0.017; tooth 41: p = 0.0049). Significant pairwise differences were primarily observed between the original dataset and specific degraded conditions involving blur and reduced spatial resolution, while several comparisons remained non-significant. Inter-examiner reliability varied across image groups and decreased notably with pronounced voxel enlargement, particularly in the periodontally compromised tooth. Conclusions: Controlled image degradation conditions of CBCT image quality significantly affect measurement outcomes and inter-examiner reproducibility of periodontal bone measurements. These findings demonstrate that image quality is a critical determinant of measurement reliability and examiner-dependent interpretation. From both a clinical and AI-development perspective, maintaining adequate CBCT resolution may contribute to more consistent measurement behavior and more reliable training datasets. Full article

Fractal dimension is widely used as a quantitative descriptor of structural complexity in digital images. However, its practical implementation often involves methodological and computational trade-offs. The compression-based estimator provides an information-theoretic formulation that operates directly on grayscale images without mandatory binarization. Although the method is theoretically grounded and has been applied in real-world scenarios, its implementation-level behavior and computational characteristics have not been systematically analyzed under controlled conditions. To address this gap, this work presents a structured GPU-enabled validation framework for this estimator using synthetic Julia sets with known theoretical fractal dimensions. By focusing on their planar boundaries, which enable direct ground-truth comparison across multiple resolutions, numerical accuracy, statistical stability, and execution time are jointly evaluated across CPU and GPU implementations. Furthermore, additional experiments assess sensitivity to progressive Gaussian blur and exploratory behavior on grayscale textures from the Brodatz dataset, revealing that boundary-dominated fractals consistently yield dimensions between 1 and 2, whereas volumetric textures produce values greater than 2 without modifying the estimation framework. Performance profiling identifies distinct computational regimes and highlights a trade-off between robustness and execution time in the double-compression GPU configuration. This approach establishes a reproducible evaluation framework that supports the practical deployment of compression-based fractal dimension estimation in large-scale and time-constrained image analysis systems. Full article

To address challenges such as low contrast, complex backgrounds, and discontinuous crack distribution in coating surface microcrack detection, a detection method combining circular neighborhood features with an improved U-net is proposed. In the preprocessing stage, a background template is constructed via median filtering, and crack contrast is enhanced through a combination of difference operations and Gaussian smoothing. Based on the spatial aggregation and directionality of crack pixels, multi-scale and multi-directional circular scanning filters were constructed to generate neighborhood difference maps for quantifying the crack distribution probability. The ImF-Att-DO-U-net was designed by utilizing a dual-channel input consisting of the original image and the crack probability map. The encoder embeds lightweight CBAMs to strengthen crack features, while the decoder introduces DO-Conv and Leaky ReLU to enhance detail capture capabilities. A hybrid loss function combining Binary Cross-Entropy and Dice loss was employed to optimize class imbalance. Algorithm testing results demonstrate that the proposed method achieved a Dice coefficient of 0.884, an SSIM of 0.893, and an accuracy of 0.911, outperforming comparative models such as DO-U-net. The extraction rate for cracks ≥10 μm reached 98%, with a minimum detectable crack size at the 7 μm level. The method exhibited excellent robustness under noise and blur testing, demonstrating superior environmental adaptability. Full article

Soymilk solid content (%) is a critical quality indicator that is directly related to product classification and regulatory compliance in food manufacturing. However, conventional optical refractometer-based measurements often suffer from blurred scale boundaries and subjective reading errors, leading to poor reproducibility under varying illumination conditions. This study proposes an image-based signal analysis framework that quantitatively interprets blurred liquid-scale boundaries by analyzing pixel intensity profiles, their gradients, and effective boundary widths. Instead of relying on human visual judgment, the proposed method characterizes boundary uncertainty using Gaussian-smoothed intensity signals and derivative-based feature extraction. Quantitative validation against ground-truth concentration values over 150 images demonstrates an overall mean absolute error (MAE) of 1.90 and a root mean squared error (RMSE) of 3.85. Illumination conditions yielding stable, single-peak derivative responses achieve an overall MAE of 0.23, whereas severe illumination conditions associated with unstable or distorted derivative patterns result in substantially higher errors (MAE = 8.57, RMSE = 8.60). These results quantitatively confirm that derivative-based boundary signal stability is directly linked to measurement accuracy. By transforming visual ambiguity into quantifiable signal features, this work provides a practical and reproducible alternative to subjective refractometer readings and offers a foundation for reliability-aware optical concentration measurement systems in industrial environments. 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

People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor environments. In this paper, we present a 60 GHz millimeter-wave (mmWave) radar-based occupancy monitoring system that enables accurate and privacy-preserving people counting. The proposed system leverages echo signals processed through Doppler and range spectrogram and analyzed by an enhanced ResNet-50 deep learning model to classify motion states and count individuals. Experimental results collected in a typical indoor environment demonstrate that the system achieves 95.45% accuracy across 6 classes of movements and 98.86% accuracy for people counting (0–3 persons). The method also shows strong adaptability under limited data and robustness to Gaussian blur interference, providing an efficient and reliable solution for intelligent indoor occupancy monitoring. Full article

Apple leaf disease identification with high precision is one of the main challenges in precision agriculture. The datasets usually have class imbalance problems and environmental changes, which negatively impact deep learning approaches. In this paper, an ablation study is proposed to test three different scenarios: V1, a hybrid balanced dataset consisting of 10,028 images; V2, an imbalanced dataset as a baseline consisting of 14,582 original images; and V3, a 3× physical augmentation approach based on the 14,582 images. The classification performance of YOLOv11x was benchmarked against three state-of-the-art CNN architectures: ResNet-152, DenseNet-201, and EfficientNet-B1. The methodology incorporates controlled downsampling for dominant classes alongside scenario-based augmentation for minority classes, utilizing CLAHE-based texture enhancement, illumination simulation, and sensor noise generation. All the models were trained for up to 100 epochs under identical experimental conditions, with early stopping based on validation performance and an 80/20 train-validation split. The experimental results demonstrate that the impact of balancing strategies is model-dependent and does not universally improve performance. This highlights the importance of aligning data balancing strategies with architectural characteristics rather than applying uniform resampling approaches. YOLOv11x achieved its peak accuracy of 99.18% within the V3 configuration, marking a +0.62% improvement over the V2 baseline (99.01%). In contrast, EfficientNet-B1 reached its optimal performance in the V2 configuration (98.43%) without additional intervention. While all the models exhibited consistently high AUC values (≥99.94%), DenseNet-201 achieved the highest value (99.97%) across both V2 and V3 configurations. In fine-grained discrimination, the superior performance of YOLOv11x on challenging cases is verified, with only one incorrect classification (Rust to Scab), while ResNet-152 and DenseNet-201 incorrectly classified eight and seven samples, respectively. Degradation sensitivity analysis under controlled Gaussian noise and motion blur indicated that CNN baseline models maintained stable performance. High minority-class reliability, including a 96.20% F1-score for Grey Spot and 100% precision for Mosaic, further demonstrates effective fine-grained discrimination. Results indicate that data preservation with physically inspired augmentation (V3) is better than resampling-based balancing (V1), especially in terms of global accuracy and minority-class performance. Full article

Accurate state estimation is essential for the performance of uncalibrated visual servoing systems, yet it is frequently undermined by non-Gaussian disturbances—such as impulse noise, motion blur, and occlusions—whose heavy-tailed statistical characteristics are not adequately represented by conventional Gaussian models. To address this issue, this paper presents an evolutionary neural-enhanced intelligent controller designed for robotic visual servoing under such noise conditions. The controller architecture incorporates a hybrid estimation core that integrates $\alpha$-stable distribution modeling for principled noise characterization with an Interacting Multiple Model Kalman filter (IMM-KF) to address system dynamics and uncertainties. A multi-layer perceptron (MLP), optimized globally via the Stochastic Fractal Search (SFS) algorithm, is embedded to provide adaptive compensation for residual estimation errors. This integration of statistical modeling, adaptive filtering, and evolutionary optimization constitutes a coherent learning-based control framework. Simulations and physical experiments reveal that the proposed method enhances improvements in estimation accuracy and tracking performance relative to conventional approaches. The outcomes indicate that the framework offers a functional solution for vision-based robotic systems operating under realistic conditions where non-Gaussian sensor noise is present. Full article

Convolutional neural networks leverage their efficient ability to extract common features of images, playing a crucial role in numerous computer vision tasks. Key details such as edges and textures in images often present themselves in the form of high-frequency components, which contain rich semantic information and are essential for accurate image recognition and understanding. However, during the downsampling process, these high-frequency components are improperly mapped to low-frequency components, leading to signal aliasing. This aliasing results in the loss of image detail information and blurred features, significantly affecting the precise extraction of image features by convolutional neural networks and ultimately reducing the performance of the model in various tasks. To effectively address this challenge, this study innovatively proposes the Gaussian Filter Convolution (GFC) module. This module ingeniously utilizes convolution kernels with filtering functions, which can specifically suppress the high-frequency components in the image, reducing the occurrence of signal aliasing at its source, thereby significantly alleviating the aliasing artifacts generated during downsampling. Experimental data show that the model integrated with GFC has significant improvements in key indicators such as model accuracy. Full article

Multispectral (MS) satellite imagery provides rich spectral information for surface and atmospheric interpretation, yet its spatial resolution is often limited by sensor design. In this study, we propose a Transformer-based MS super-resolution framework that uses high-resolution panchromatic (PAN) imagery to supply complementary spatial detail cues for MS reconstruction and explicitly separates spatial enhancement from spectral preservation. In the spatial branch, PAN features are aligned to the MS grid via Pixel-Unshuffle and encoded with shifted-window self-attention to capture long-range spatial dependencies efficiently. In the spectral branch, spectral self-attention treats bands as tokens to learn inter-band correlations and maintain spectral consistency. The two representations are fused through channel concatenation and a 1 × 1 convolutional module, followed by a reconstruction head that upsamples the fused features to generate high-resolution MS outputs. For training, low-resolution MS inputs are synthesized from KOMPSAT-3A MS imagery using a degradation pipeline that combines modulation transfer function-based blur, downsampling, and additive Gaussian noise; the operation order is randomly permuted to emulate diverse acquisition conditions. In addition, Bayesian optimization is employed to explore network configurations through jointly considering the normalized mean absolute error and inference time. Experiments demonstrate that the proposed approach attains 46.23 dB PSNR, 0.9735 SSIM, and 3.12 ERGAS with approximately 167.4 K parameters, achieving a high restoration quality and computational efficiency across diverse degradation settings. Full article

Background/Objectives: Accurate nuclear segmentation is a fundamental step in computational pathology, enabling reliable estimation of cellularity and nuclear morphology. However, segmentation models are typically evaluated under ideal imaging conditions, while real-world microscopy data are affected by staining variability, noise, and image degradation. This study aims to comparatively evaluate three representative convolutional architectures for nuclei segmentation, with emphasis on robustness and clinical relevance under perturbed imaging conditions. Methods: U-Net, Attention U-Net, and U-Net++ were trained and evaluated on the BBBC038 nuclei microscopy dataset using fixed train–validation–test splits. Robustness was assessed under three types of synthetic perturbations: Gaussian blur, additive noise, and color jitter. Segmentation performance was quantified using the Dice coefficient and Intersection-over-Union (IoU). Paired Wilcoxon signed-rank tests with Holm correction and Cliff’s delta were used for statistical comparison. In addition, clinically relevant nuclear descriptors—nuclear count, median nuclear area, area interquartile range (IQR), and nuclear density—were extracted from predicted masks, and descriptor stability was analyzed as relative deviation from clean conditions. Results: Under clean imaging conditions, Attention U-Net achieved the highest mean Dice score, while paired statistical analysis indicated that U-Net++ exhibited the most consistent performance across test samples. Under image perturbations, Attention U-Net demonstrated greater robustness to blur and noise, whereas U-Net++ showed superior stability under color variations. Descriptor-based analysis further indicated that U-Net++ preserved nuclear count and density most reliably under chromatic perturbations, while U-Net exhibited larger instability in nuclear count and density, particularly under noise. Conclusions: Architectural design choices strongly influence not only pixel-level segmentation accuracy but also the stability of clinically relevant nuclear morphology descriptors. Robustness evaluation under multiple perturbation types reveals important trade-offs between architectures that are not captured by clean-image benchmarks alone. These findings highlight the necessity of multi-level evaluation strategies combining overlap metrics, statistical testing, robustness analysis, and descriptor stability assessment for future benchmarking and clinically reliable deployment of nuclei segmentation systems. Full article

As China’s urban–rural integrated development strategy advances, traditional urban–rural boundaries are undergoing rapid restructuring. However, it remains unclear whether these boundaries are becoming distinct through factor flow or blurring due to urban–rural functional coupling. To address this, this study examines the dynamic evolution of boundaries across three core dimensions—population, land, and function—to evaluate the efficacy of integration. We employ the Land Continuity Index, POI Diversity Index, and Gaussian Smoothing Index to characterize transitions in land use structure, spatial functional complexity, and population gradients, respectively. Additionally, a comprehensive Urban–Rural Fuzziness Index (URFI) is developed to quantify boundary blurring trends. Results indicate that Zhengzhou’s urban–rural boundaries exhibit a sustained weakening trend. Notably, changes in functional and population dimensions significantly outpace the land dimension, identifying functional urbanization and population mobility as the primary drivers of this blurring. Consequently, the URFI serves as a robust indicator of integration effectiveness. Overall, boundary blurring is not merely an external manifestation of urban expansion but a profound outcome of factor reorganization, spatial optimization, and the reshaping of urban–rural relationships. This study provides a novel quantitative tool for assessing policy effectiveness, offering both theoretical insights and practical implications for understanding urban–rural integrated development. Full article