Journal of Image and Graphics aims to publish original, previously unpublished, research, reviews, communication, plus case studies and short research notes, on both applied and theoretical aspects in imaging and graphics research.
AI-driven Saliency Modeling for Quantifying Visual Engagement in Tourism Media
Quantifying visual attention in tourism promotional videos is crucial for enhancing digital communication efficiency. To enhance digital communication efficiency in the highly competitive global tourism marketplace, this research explores how AI-assisted visual analysis can optimize audience engagement. This research proposes a computational analysis framework utilizing a Convolutional Neural Network (CNN) integrated with the DeepGaze II architecture to predict saliency patterns in Thai tourism advertising campaigns. Visual features are extracted at the frame level using technical metrics, including the Weber contrast ratio (ΔL/L), optical flow magnitude, and Hue, Saturation and Value (HSV) color histograms. The system classifies viewer engagement into three attention tiers—High Attention (HAT), Medium Attention (MAT), and Low Attention (LAT)—based on simulated gaze density. Empirical results indicate that compositions with a high Weber contrast (ΔL/L > 1.5) and complex cultural symbols significantly correlate with attention scores exceeding 0.6. Furthermore, rhythmic pacing analysis reveals that alternating saliency levels contribute to a 19–23% increase in attention retention. Additionally, age-stratified gaze simulations demonstrate distinct responses to motion energy across different demographic groups. This study establishes an empirical link between visual formalism and computer science, providing a predictive tool for optimizing high-impact digital media design.
Saliency Prediction, Visual Attention Modeling, Weber Contrast, Optical Flow, Image Analysis
Copyright (c) 2026 Journal of Image and Graphics
Hybrid CNN-DNN Facial Expression Recognition in an Affective Virtual Reality (VR) and 360° E-learning Platform
Immersive Virtual Reality (VR) is increasingly used to support experiential learning and social presence in higher education, while 360° video offers lightweight immersive experiences accessible from standard browsers and mobile devices. However, few platforms integrate deep learning-based Facial Expression Recognition (FER) with VR and 360° e-learning in a unified architecture tailored for distance education. This paper presents an integrated e-learning platform that combines: a Unity-based multi-userVR classroom, a web-accessible 360° video learning module,and a hybrid Convolution Neural Network-Deep NeuralNetwork (CNN-DNN) FER service that fuses appearancecues from grayscale face images with geometric descriptorsfrom facial landmarks. We describe a proof-of-conceptimplementation and evaluate the FER component on publicdatasets (Cohn-Kanade dataset (CK+), Japanese FemaleFacial Expression (JAFFE), University of Oulu-Institute ofAutomation, Chinese Academy of Sciences (OULU-CASIA))using subject-independent splits, and we report anexploratory pilot study with 65 university instructors whoexperienced a short VR lecture scenario with FER-drivenavatar facial animation and provided usability andperception feedback. The fused CNN-DNN achieved 100%accuracy on CK+ (6- and 7-class settings), 88.89% onJAFFE (6 classes), and 81.25% on OULU-CASIA(6 classes), while cross-dataset performance dropped to47.89% in the OULU→JAFFE setting, highlightingremaining generalization challenges. In the pilot study, 74%of instructors agreed that the VR scenario supportedemotional communication, and 72% reported that avatarfacial cues helped them notice confusion or engagement,although 93% also identified substantial integrationbarriers related to cost, infrastructure, and training. Theseresults support the technical feasibility of the platform whileclarifying the practical and ethical conditions required forresponsible affect-aware immersive learning.
Reinhard-illumination Based Single Scale Retinex for Low Light Images Amelioration
Smartphones and digital cameras are essential tools for taking color images. Nevertheless, under poor lighting conditions, obtaining high-quality color images is difficult due to low brightness and uneven lighting. Various strategies have been suggested to enhance image light quality. Nevertheless, they did not produce a good result in the termsof excessive colors, halos, and loss of vital information. Therefore, the Reinhard-Illumination Based Single Scale Retinex (RISSR) algorithm is proposed in this study. The first step of the proposed algorithm involves inputting the degraded image and then predicting a single illumination map for all color channels which are altered using the Global Reinhard operator. In turn, the enhanced illumination is used to generate better color channels which are concatenated into the color image. This image is then processed with a gamma correction function and a normalization function to obtain the final improved image. The performance of the proposed algorithm was evaluated from real images under low-light conditions and judged by five metrics by comparison with ten other algorithms. Results indicated that the model presented the advantage of using the proposed algorithm with natural colors and perceived visual quality.
Single Scale Retinex, Global Reinhard Operator, Low-light Images
Contrastive Vision Transformer Combined with Hyperparameter Fine-Tuning and Interpretable AI for Glaucoma Assessment
Glaucoma, often known as the ‘silent thief of sight’, is a leading cause of irreversible blindness, which affects around 80 million people worldwide and hence its early and reliable detection is critical for preventing permanent vision loss. Although Vision Transformer (ViT)-based deep learning models are employed by existing techniques for automated glaucoma screening, yet, they primarily rely on conventional supervised training and single-dataset evaluation, which result in limited feature discrimination, suboptimal generalization across heterogeneous images, and poor clinical interpretability. To address these limitations, this study proposes a novel contrastive learning-optimized ViT framework, which integrates supervised contrastive pre-training with systematic hyperparameter optimization to learn more discriminative retinal features, and fine-tuning for glaucoma classification. In addition, a unified preprocessing and patch-based representation strategy is introduced to mitigate domain shifts across multiple imaging devices and acquisition protocols. Unlike prior studies using single benchmarks, this framework is validated on comprehensive multi-dataset setting combining six public fundus datasets (including G1020, ORIGA, REFUGE, PAPILA) to assess real-world generalization. Experimental results demonstrate consistent and statistically significant improvements over Convolutional Neural Network (CNN), baseline ViT models, in terms of achieving up to 87.91% accuracy and performance gains of 3-16% across accuracy, precision, recall, and F1score metrics. Further, Layer-wise Relevance Propagation (LRP) is employed to generate clinically interpretable heatmaps, which confirms that the model focuses on anatomically meaningful regions such as the optic disc and optic nerve head. These findings prove that the proposed framework provides robust, explainable, and generalizable solution for automated glaucoma screening and highlights its potential for clinical deployment.
Topic: Machine Learning Based Techniques for Image and Video Processing
Robust Hybrid Deep Learning and Filtering Model for Occlusion Underwater Object Tracking
Underwater object detection plays a vital role in marine exploration, surveillance, and ecological monitoring. However, the presence of light scattering, color distortion, and low visibility makes accurate detection and tracking in underwater environments extremely challenging. To address these issues, this work proposes an efficient underwater object detection and tracking framework integrating the Single Shot MultiBox Detector (SSD), Deep Simple Online and Realtime Tracking (SORT), and Kalman Filter (KF). The SSD model is employed for real-time object detection due to its capability of multi-scale feature extraction and fast inference. Detected objects are then tracked across frames using the Deep SORT algorithm, which combines appearance descriptors and motion information for robust identity preservation. To further enhance trajectory estimation and reduce noise, the Kalman Filter is incorporated to predict object positions in occluded or visually degraded frames. The proposed hybrid approach demonstrates improved stability and accuracy in underwater video sequences utilizing the benchmark Underwater Object Tracking (UOT32) dataset. Performance evaluation based on precision, recall, and F1-Score confirms the model’s robustness in identifying and tracking objects under varying underwater conditions. The obtained results with a precision of 97.39%, a recall 100%, and F1-Score of 0.986 indicate that the integration of SSD with Deep SORT and Kalman filtering offers a reliable and computationally efficient solution for real-time underwater detection and tracking applications.
Clustering-integrated Horizontal Pyramid Feature Embedding for In-domain Unsupervised Person Re-identification
A cross-domain and unsupervised Re-Identification (Re-ID) is primarily impacted by the challenges presented by domain shift, intra-class heterogeneity across datasets, and the lack of annotated target pictures. When training and testing take place within the same dataset, these influences are lessened in the in-domain context. In real-time, scalable systems, fine-grained part-based representations like unsupervised horizontal pyramid similarity learning struggle to capture adaptive cluster semantics and integrate generalization. Cluster Integrated Horizontal Pyramid Feature Encoding (CI-HPFE), a deep learning architecture, addresses these issues in this study. Horizontal pyramid-based feature extraction encodes high-resolution local and global body components with key improvements in our model. First, the Pyramid-based Horizontal Part Feature Encoder (PHPFE) splits spatial feature maps into multi-scale horizontal sections (levels 1, 2, 4, 8) and extracts robust, granular embeddings via stripe-wise pooling and processing. Besides feature extraction, a cluster-aware embedding module incorporates unsupervised clustering feedback into representation learning. Identity representations can change with cluster centroids. To achieve alignment and discriminability, our hybrid loss function combines local-part granularity with global identity coherence using triplet margin loss, entropy minimization, and cluster consistency loss. To reduce domain shift, Stripe encoders use a Domain-Adaptive Instance Normalization (DAIN) module to learn domain-conditioned affine statistics. For distributed deployment, a Federated Cluster Synchronization Mechanism (FCSM) synchronizes embedding spaces among edge devices without sharing raw picture data, boosting scalability, privacy, and speed. Benchmark datasets use is Market-1501 and DukeMTMC-ReID, which show considerable performance gains, including 5–6% accuracy, ~6% mAP uplift, and enhanced resilience during in-domain assessment. For homogeneous-domain person re-identification systems, the suggested architecture thus strikes a balance between methodological familiarity, unique functional modules, and deployment-level practicality.
Person Re Identification, Horizontal Pyramid Encoding, Unsupervised Clustering, Domain Adaptation, Deep Feature Embeddings
Explainable Deep Learning for Cervical Cancer Cell Classification: A Comparative Analysis of CNN Architectures with DenseNet-121
Pap-smear cytology is essential in the detection of cervical cancer at an early stage, but manual cervical cancer examination is time-consuming, subjective, and liable to diagnostic variability. Deep learning provides an alternative (automatic) method based on cytology. The paper introduces an explainable deep-learning architecture to classify cervical cells and the comparison of nine popular Convolutional Neural Networks (CNN) models, namely DenseNet, ResNet, Visual Geometry Group (VGG), Inception, EfficientNet, and a custom CNN-18 model. Our collection contained 6374 pictures of five epithelial classes, used standardized preprocessing and massive augmentation, and trained all the models using the same pipeline. The models were tested based on accuracy, precision, recall, F1-Score, and Area Under Curve (AUC). The highest performance was obtained by DenseNet-121, which has an accuracy of 98.85% and an F1-Score of 0.9885. It was much better than more complicated models, which were proven using ANOVA (p < 0.05). Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations indicated the model is always interested in clinically pertinent nuclear and cytoplasmic regions and, therefore, enhances transparency and facilitates expert interpretability. These results indicate that DenseNet-121 offers an accurate, computationally efficient, and understandable solution that can be used in cervical cancer screening, which is supported by Artificial Intelligence (AI). The framework could be implemented in digital pathology workflows and applied in various clinical settings.
Enhancing Autism Classification Accuracy on Facial Images Using a CNN Model Decision Fusion
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition affecting social interaction, communication, and behavior. Early detection is critical, yet conventional diagnostic methods are often time-consuming and resource-intensive. This study proposes a deep learning framework based on decision fusion of multiple Convolutional Neural Network (CNN) architectures to classify facial images of children with and without ASD. Six architectures (ResNet, DenseNet, Xception, ShuffleNet, MobileNetV2, and EfficientNet) were fine-tuned through transfer learning, and their predictions were integrated via decision fusion to improve generalization and classification accuracy. Experiments were conducted on a primary dataset of 1,050 facial images collected from 70 children (35 ASD, 35 typically developing) aged 5 to 15 years, recruited from special and regular elementary schools in Banda Aceh, Indonesia. Data augmentation was applied to address the limited sample size. Model performance was evaluated using accuracy, sensitivity, specificity, precision, and F1-Score. Among the combinations tested, ResNet and EfficientNet together yielded the best results, achieving 90% accuracy and a 91% F1-Score. These findings suggest that decision fusion is a practical and scalable approach for early ASD screening in resource-limited settings.
Analyzing the Impact of Preprocessing on Deep Learning-based Brain Tumor Classification Using Glioblastoma Magnetic Resonance Image
Glioblastoma is the most aggressive primary brain tumor and demands reliable image-based analysis for accurate interpretation. This study analyzes how different magnetic resonance image preprocessing strategies affect visual representation learning and deep feature discrimination in deep learning-based glioblastoma detection. An EfficientNetB0 convolutional neural network with transfer learning was employed to learn hierarchical image features from brain magnetic resonance images. The dataset consisted of 4465 images spanning 15 tumor classes, including 204 glioblastoma cases. Five preprocessing scenarios were evaluated: baseline (no preprocessing), contrast enhancement (Contrast Limited Adaptive Histogram Equalization (CLAHE)), image denoising, skull stripping, and intensity normalization. The results indicate that intensity normalization yields the most stable and discriminative feature representations, achieving the highest accuracy of 89.70% with balanced glioblastoma sensitivity and precision. Conversely, aggressive preprocessing methods distort image intensity distributions and spatial cues, leading to degraded feature learning and reduced performance. Visual interpretability analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) further demonstrates that intensity normalization enables consistent attention to tumor-relevant regions while preserving critical morphological structures. These findings highlight that simple intensity standardization is more effective than complex enhancement techniques for robust and visually meaningful glioblastoma image analysis.
Cloud-Net: A Lightweight Convolutional Framework with Spectral-spatial Feature Enhancer for Real-time Brain Tumor Segmentation
Accurate brain tumor segmentation supports clinical decision-making, including treatment planning and surgical navigation, yet manual delineation is time-consuming and labor-intensive. Many high-accuracy models remain difficult to deploy under latency and memory constraints, while multimodal Magnetic Resonance Imaging (MRI) introduces severe foreground-background imbalance. Cloud-Net is a lightweight U-shaped convolutional framework that places a Spectral-Spatial Feature Enhancer (SFFE) at the bottleneck, combining a spatial convolution branch with Fourier-domain feature modulation to strengthen global structural cues while preserving local boundary detail. The model is optimized with a hybrid Dice-Focal objective and evaluated on the Brain Tumor Segmentation 2020 (BraTS 2020) dataset using T1-weighted (T1), contrast-enhanced T1-weighted (T1ce), T2-weighted (T2), and Fluid-Attenuated Inversion Recovery (FLAIR) MRI sequences. On tumor present slices, Cloud-Net achieves a Dice Similarity Coefficient (DSC) of 0.7701 and an Intersection over Union (IoU) of 0.6825, with a 95th-percentile Hausdorff Distance (HD95) of 20.21 pixels, while maintaining real-time efficiency at 2.73 ms/slice and 365.64 Frames Per Second (FPS) using 9.35 million parameters (35.69 MB). Ablation analysis indicates that SFFE improves overlap and boundary quality, and Gradient-weighted Class Activation Mapping (Grad-CAM) highlights lesion-relevant regions influencing predictions. These results support Cloud-Net as a practical option for real-time brain tumor segmentation in cloud-edge-oriented workflows.
Brain tumor segmentation, Multimodal MRI, Lightweight CNN, Spectral–spatial fusion, Grad-CAM
A Controlled Comparison of BiLSTM and Transformer Encoders for Arabic Handwritten Word Recognition
Despite significant advances in Arabic Handwritten Word Recognition (AHWR), the specific contribution of sequential encoders remains unclear because most prior studies change several architectural elements simultaneously, making comparisons difficult to interpret. This work presents the first carefully controlled evaluation of Bidirectional Long Short-Term Memory (BiLSTM) and Transformer encoders within an identical Convolutional Neural Network-Connectionist Temporal Classification (CNN-CTC) framework. All external factors, including preprocessing, the truncated ResNet-50 backbone, CTC alignment, Word Beam Search (WBS) decoding, and dataset splits, are held constant to isolate the effect of the sequence modeling mechanism itself, a dimension not explicitly analyzed in previous literature. The study also evaluates three augmentation strategies, including uniform augmentation and frequency-aware schemes at both the word and character levels, which address the distributional imbalance inherent in Arabic handwriting datasets. Experiments on IFN/ENIT show that the Transformer consistently achieves higher accuracy, up to 98.91%-Character Accuracy (CAR) and 98.41%-Word Accuracy (WAR), while the BiLSTM offers substantially faster inference. These findings provide the first reproducible quantification of the accuracy-efficiency trade-off between recurrent and attention-based encoders for Arabic cursive handwriting under fully controlled conditions.
Arabic Handwritten Word Recognition, ResNet-50, Transformer Encoder, Attention Mechanism, Connexionist Temporel Classification, Word Beam Search, Data Augmentation
FLARE: Fisheye Lens Anchor-based Radial Enhancement for Internal Vibration Compensation in Low-cost Drones
Internal micro-vibrations of fisheye lenses in low-cost drones or small Unmanned Aerial Vehicles (sUAV) are a significant problem in aerial imaging systems. With an altitude of 120 m, even a ±5-pixel shift on a 0.8 MegaPixel (MP) image may lead to more than 2 m ground displacement, affecting downstream tasks such as segmentation or object detection. There is a significant body of research addressing global vibrations, which affect the entire camera platform, e.g., sUAV. However, the problem of digital stabilization offisheye lenses against internal jitter is under-explored, whichcan induce significant ground displacements, especially onlow-cost fisheye cameras. Therefore, we introduce FisheyeLens Anchor-Based Radial Enhancement (FLARE), astabilization framework using the fisheye vignette center as astabilization reference. Our main contributions are: (1)Gaussian Radial Mask (GRM), a spatially-varying blurtechnique that removes visual distractors and preservesrelevant vignette boundary, and (2) Geometric AnchorCenter (GAC), a coordinate prediction mechanism thatanchors the stabilization reference within a reduced range,thus enabling sub-pixel stabilization accuracy. To takeadvantage of GAC, we formulate an anchor-aware loss thatencodes the prior knowledge of the stabilization referencelocation. We use the predicted vignette center to estimate thevibration displacements from an arbitrary point (e.g., theimage center), and subsequently stabilize the frame byapplying an inverse motion through affine imagetransformations. We test on standardized image resolutionsof 0.8 MP and report promising results, including a mean of0.221 pixels, with 75% of center prediction errors below 0.3pixels and 95% errors under 0.48 pixels. Additionally, weconduct ablation experiments and show that removal of GACand GRM increases the 95-th percentile from 0.481 to 1.131pixels.
Brain Fog Consortium Image Analysis Through Internet of Things Using Fog Computing in Stratus Cloud
The functioning of human organs is regulated by the central hub of the brain. Brain fog is characterized by a sensation of mental cloudiness. It affects the quality of life, memory impairment, and Alzheimer’s disease. Recent investigations have been conducted for the analysis of the brain fog consortium. Brain fog is defined as confusion, diminished mental clarity, forgetfulness, and impaired concentration. Also, it signifies a shortage in nutrients, a sleep disorder, depression, or a thyroid disease. This research proposes the identification of a brain fog consortium utilizing the Internet of Thing (IoT) and fog computing, alongside the suggested stratus cloud system for the brain through IoT integration. It evaluates cognitive impairments, categorizes genuine diseases, and performs analyses using a fog computing platform. The proposed study focuses on investigating segmentation diseases and their excision from the brain’s orientational organum, along with prospective detection methodologies within the anatomical structure. The reduction of brain fog can be achieved through the rapid advancement of IoT cloud computing and fog computing services. Experimental results, derived from the data generation of the proposed method, demonstrate efficiency levels in terms of density, affinity ratios, energy consumption, and storage relative to compute time for detecting brain fog. The accuracy of the propagated results is notable, and the implementation within the fog computing gateway is of high qualitative standard.
Comparative Analysis of EfficientNet-B0 and MobileNetV3-large Architectures for Imbalanced Multiclass Skin Lesion Classification
Automated skin lesion classification systems face significant challenges in handling severe class imbalance characteristic of clinical dermatological datasets. This study presents a comprehensive comparative analysis of two state-of-the-art convolutional neural network architectures—EfficientNet-B0 and MobileNetV3-Large—for multiclass skin lesion classification on the HAM10000 benchmark dataset. The investigation systematically addresses an extreme class imbalance ratio of 58.30:1 through the strategic application of RandomOverSampler methodology combined with transfer learning from ImageNet pretrained weights. We implement identical training protocols for fair architectural comparison, including progressive dropout regularization (0.3→0.2→0.1) and conservative data augmentation strategies. Comprehensive ablation studies demonstrate the cumulative effectiveness of each pipeline component, with class balancing contributing 10.39% improvement and transfer learning providing substantial performance gains. Experimental validation reveals that EfficientNet-B0 achieves 98.86% classification accuracy with balanced precision, recall, and F1-Scores of 98.89%, 98.86%, and 98.85%, respectively, while MobileNetV3-Large delivers comparable performance at 98.88% accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis validates that learned features align with clinical diagnostic criteria, demonstrating attention to lesion boundaries, color variations, and texture patterns. Compared to existing state-of-the-art methods on HAM10000, our approach achieves superior performance with fewer parameters (5.3 M), representing a marked advancement for resource-constrained deployment scenarios. These findings establish a validated framework for managing severely imbalanced medical imaging datasets and provide empirical guidance for efficient architecture selection in dermatological computer-aided diagnosis systems.
Topic: Deepfake Detection and Image Processing
Feature Selection using Adaptive Mutation Probability based Starfish Optimization Algorithm in Diabetic Retinopathy Classification
Diabetic retinopathy (DR) is a common ocular complication of diabetes and a leading cause of vision impairment. Grading the severity of DR plays a crucial role in early diagnosis and timely intervention. However, manual diagnosis is often time-consuming, resource-intensive, and susceptible to human error. In contrast, computer-aided diagnosis (CAD) systems provide faster, more consistent, and potentially more accurate alternative, while enabling more effective screening and treatment planning. Thus, this research proposes the feature selection approach of Adaptive Mutation Probability based Starfish Optimization Algorithm (AMP-SFOA) for the automatic diagnosis and classification of DR into multiple classes. Primarily, this research considers two standard dataset, ATPOS-2019 and DDR to implement the proposed method based on training and testing. Then, Image resizing, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Data Augmentation are performed to improve the input data. Finally, the Long Short-Term Memory (LSTM) is used for the classification of DR into multi-classes. The experimental results demonstrates that the proposed AMP-SFOA approach reaches better accuracy of 99.476% and 96.958% on APTOS-2019 dataset and DDR dataset respectively as compared to the existing methods such as RT2Net and Swin-TransformerV2 Multi-Branch Fine-grained Diabetic Retinopathy Grade Classification (STMF-DRNet).
contrast limited adaptive histogram equalization
Gaussian Splatting for Photorealistic Immersive Virtual Tours
To achieve photorealistic Virtual Reality (VR) tours, a balance is required between visual fidelity, responsiveness, and the strict runtime and memory constraints of head-mounted displays. As a more recent explicit view synthesis method, three-Dimensional Gaussian Splatting (3DGS) has been proposed as a real-time alternative to Neural Radiance Fields (NeRF). In this study, we present an overview of 3DGS, demonstrating its application to immersive VR tours, and propose a design philosophy for its implementation on standalone headsets. Specifically, we review state-of-the-art 3DGS variants with respect to photorealism, scalability, efficiency, dynamics, and semantics; analyze VR-specific constraints related to latency, stereo stability, and thermal, memory, and interaction requirements; collate VR style datasets which are suitable for training and benchmarking 3DGS-based pipelines; present case studies in domains such as museums, real estate walkthroughs, and large-scale campus tours; synthesize an evaluation framework that organizes metrics for visual quality, performance, memory usage, and interaction responsiveness. The evaluation framework has four axes: visual fidelity, runtime performance, memory footprint, and interaction responsiveness. Although more recent accelerated NeRF-based systems (e.g., Instant-Neural Graphics Primitives (Instant-NGP) and baked radiance fields) directly reduce the performance difference between 3DGS and explicit methods, 3DGS still provides better trade-offs in interactive tours, such as predictable latency and instant editability. Finally, open challenges in streaming, reflectance, semantics, and authoring ergonomics are outlined, and future directions for 3DGS-based VR tour systems are discussed.
Special Issue: Advancements in AI-Driven Image Processing and Graphics: Techniques, Applications, and Challenges
Comparative Analysis of Loss Functions for Semantic Segmentation: An Empirical Study on Cityscapes Dataset
Semantic segmentation remains a fundamental challenge in computer vision, where the choice and weighting of loss functions significantly impact model performance. This study presents a comprehensive comparative analysis of individual versus combined loss functions with systematic weight ablation for semantic segmentation using modified Attention U-Net and DeepLabV3+ architectures on the Cityscapes dataset. We systematically evaluate seven weight configurations across three loss components (Cross-Entropy, Dice, Focal) through rigorous ablation studies, and validate our findings across two architectures to ensure generalizability. Through extensive experimentation across 20 epochs with 2,975 training and 500 validation images, our results demonstrate that the Dice-dominant weighting configuration (0.5:1.0:0.5 for CE:Dice:Focal) achieves superior performance with 57.83% mean Intersection over Union (mIoU) on Attention U-Net and 58.35% mIoU on DeepLabV3+, representing 7.78% improvement over the best individual loss function. Comprehensive ablation studies reveal that weight configuration critically affects performance, with Dice-dominant weighting consistently outperforming equal weighting (55.59% mIoU) and individual loss functions. Qualitative analysis demonstrates substantial improvements in boundary delineation and small object detection, with boundary IoU improving by 1.41% and challenging class performance (trucks, pedestrians) improving by 5–21%. Statistical analysis reveals that Cross-Entropy provides the most efficient training with a 75.4% loss reduction, while Dice loss exhibits convergence challenges, resulting in only a 34.5% reduction. Our findings conclusively demonstrate that optimized combined loss function weighting achieves better segmentation performance than both individual approaches and naive equal weighting strategies, with consistent improvements across different network architectures.
Domain-Aware Spatial-Temporal Selective Gated LSTM for Real-Time Driver Drowsiness Detection
Driver drowsiness remains a critical cause of road accidents, creating a strong demand for real-time, non-intrusive monitoring systems deployable under practical driving conditions. Vision-based models, particularly CNN–LSTM architectures, have shown effectiveness in temporal behavior analysis, yet they degrade under illumination variation, facial occlusion, and transient noise. This limitation stems from insufficient temporal discrimination and the absence of domain-aware physiological cues, which reduces a model’s ability to capture subtle microsleeps when spatial textures are unreliable. To address these challenges, this study introduces a domain-aware spatial–temporal selective-gated LSTM framework. The model fuses deep spatial representations extracted by ResNet50 with physiologically validated indicators, Eye Aspect Ratio, Mouth Aspect Ratio, and head pitch, forming a unified 2051-dimensional sequence representation. SG-LSTM incorporates two complementary gating modules: a Channel-Selective Gate that suppresses redundant channels and a Temporal-Selective Gate that emphasizes informative time steps, preserving salient micro-expression transitions. Experiments on the NTHU-DDD benchmark, supported by real driving data, show that SG-LSTM achieves 90.09% accuracy and an F1-score of 0.90, outperforming CNN–LSTM and vanilla LSTM baselines. Deployment on an Orange Pi 3 confirms efficiency on resource-constrained edge hardware. The results demonstrate that selective temporal gating combined with domain-aware physiological fusion provides a lightweight yet robust solution for real-time driver monitoring. Future work will explore multimodal sensor fusion and large-scale naturalistic datasets to further enhance generalization.
spatio-temporal micro-expression analysis
A Benchmarking Framework for Fine-grained Multilabel Semantic Segmentation of Mango Fruit Surface Defects
This study presents a benchmarking framework for multilabel semantic segmentation of mango surface defects. It tackles the real-world issue of overlapping and co-occurring symptoms in agricultural images. The curated dataset includes full fruit images and detailed patches with multi-class annotations. This setup captures the biological complexity of natural crop conditions. Three existing segmentation architectures, DeepLabV3+, HRNet-MultilabelSeg, and You Only Look Once with Vision Transformer (YOLOViT)-MultilabelSeg, are compared using a unified evaluation method. The framework uses standardized metrics, including mean Intersection-over-Union (mIoU), F1-Score, precision, recall, and inference speed. DeepLabV3+ achieved the highest accuracy. Meanwhile, HRNet-MultilabelSeg provided real-time performance at 526 Frames Per Second (FPS) while maintaining competitive segmentation quality. This contribution offers a clear and reproducible benchmark, along with a practical roadmap for public, cross-domain validation and unified training goals.
Deep Learning in Agriculture, Pixel Level Defect Annotation, DeepLabV3+, HRNet-MultilabelSeg, YOLOViT-MultilabelSeg
Deep2.6GNet: A Lightweight 2.6 G FLOPs Architecture Model for Underwater Image Enhancement
High-resolution underwater imaging with rich color details and real time performance in underwater robots is crucial in Underwater Image Enhancement (UIE). However, light absorption, deep-sea, haziness, scattering, and suspended particles degrade underwater image quality. It is leading to low contrast and color distortion, which negatively affect visual tasks. In UIE, deploying models on mobile platforms and embedded devices has challenges due to their limited computational capacity. Therefore, a lightweight and computationally efficient model is required to enhance underwater images in resource-constrained underwater robots. To overcome this issue, this paper introduces Deep2.6GNet, a lightweight U-shaped encoder-decoder architecture model. Unlike existing lightweight models, Deep2.6GNet proposes a novel integration of Depthwise Separable Convolution (DWSConv) with the simple Attention Module (SimAM). It significantly reduces Floating-point Operations Per second (FLOPs) and the number of parameters and computational demands in UIE. Moreover, experimental evaluations on the high Structural Similarity Index Measure (SSIM) and Underwater Image Quality Measure (UIQM) scores and competitive Peak-Signal-to-Noise (PSNR) values, confirmed that Deep2.6GNet offers an excellent balance between computational efficiency and enhancement quality. Deep2.6GNet outperforms other State-of-the-Art (SOTA) methods with only 2.6G FLOPs, 0.067M parameters and 0.01s for runtime on a computer with an NVIDIA GeForce MX230 GPU, Intel® Core™ i7 processor, and 24 GB of RAM. It is well-suited for underwater robots, particularly for tasks such as mapping, target detection, and navigation.
Underwater Image Enhancement, lightweight, computational efficiency, U-Shaped encoder-decoder architecture, Depthwise Separable Convolution, SimAM module
Hierarchical Multi-Scale Transformer for Breast Tumor Staging with Visual Interpretability and Risk Stratification
Accurate tumor staging and risk stratification in breast cancer are critical for guiding treatment decisions. Traditional decision-making methods rely on whole slide images (WSIs) analysis, which is labour-intensive and subject to inter-observer variability. To address these challenges in tumor staging, we propose a modular and interpretable deep learning framework for automated tumor staging through multi-resolution histopathological analysis. Our Hierarchical Multi-Scale Transformer (HMS-T) integrates the Vision Transformers (ViTs) operating at 5×, 10×, and 20× magnifications to capture both the cellular and architectural features. In addition, a novel cross-scale attention fusion module combines these multi-scale resolution representations, enabling robust classification according to the AJCC staging system. Trained on 1,092 patients from the TCGA-BRCA cohort, our HMS-T achieves state-of-the-art performance with a staging accuracy of 91.5%, a macro F1-score of 0.89, and a quadratic-weighted kappa of 0.92, which demonstrates the strong agreement with pathological standards. Moreover, our model’s attention maps exhibit high spatial interpretability, aligning closely with expert-annotated regions (Dice score = 0.81). Ultimately, we introduced a lightweight clinical extension for preliminary survival risk stratification, achieving a concordance index of 0.74, thereby bridging the gap toward full prognostic modelling. By combining high performance with transparent decision-making, HMS-T represents a significant advancement toward deployable AI-assisted pathology tools for breast cancer.
An IoT-Enabled Intelligent Data Center Monitoring Framework Using YOLO-Based Image Analysis for Green Cities
This study presents an intelligent data center monitoring and surveillance framework for green cities, integrating Internet of Things (IoT) technology with You Only Look Once (YOLO)-based object detection. The proposed system enhances data center security and operational efficiency by enabling real-time identification of critical components such as servers, routers, cooling systems, and unauthorized personnel. Leveraging computer vision and deep learning, the framework provides proactive monitoring and rapid response to potential threats. The YOLO model is trained on a large, annotated dataset specifically curated for data center environments, allowing precise detection and localization of IoT-based objects of interest. Additional modules incorporating thermal imaging and motion detection further strengthen anomaly recognition and intrusion prevention capabilities. Performance evaluation across multiple data center scenarios demonstrates high detection accuracy, low latency, and strong robustness under varying environmental conditions. The study also highlights the computational efficiency achieved through GPU parallelization and model optimization, ensuring real-time processing in large-scale deployments. Overall, the proposed framework offers a scalable, energy-efficient, and secure solution for smart data center management, contributing to the advancement of sustainable and resilient green city infrastructures.
Object Detection, IoT, Surveillance System, Data Centre, Yolo, green cities
Video Anomaly Classification using Convolutional Neural Network with Bidirectional Long Short-Term Memory using Spatio-Temporal Adaptive Transformer
In smart cities, surveillance systems are extensively deployed to monitor public areas and critical infrastructures. These continuous video streams are the valuable source for analyzing real-time activities and supporting tasks such as object detection, behavior analysis and incident monitoring. Among these applications, detecting unusual or suspicious events is particularly crucial for ensuring safety and enabling timely responses to the threats. Because of its importance in enhancing urban security, video anomaly detection has emerged as a central research focus in the broader field of intelligent video analysis. Thus, this research proposes a new integrated framework of Convolutional Neural Networks with Bidirectional Long Short-Term Memory using Spatio-Temporal Adaptive Transformer named (CNN-BiLSTM-STAT) for the classification of video anomalies in surveillance cameras. The proposed CNN-BiLSTM-STAT was implemented by using different dataset such as UCF-Crime, RLVS, XD_Violence video and RFW-2000. Then, image resizing and label encoding techniques are used in the preprocessing phase to improve the input data. Finally, the proposed integrated classifier CNN-BiLSTM-STAT was used to classify video anomalies into multiple classes. The experimental results demonstrate that the proposed CNN-BiLSTM-STAT method attains an optimal accuracy of 99.91% on the UCF-Crime dataset compared to the existing methods such as recurrent neural networks with LSTM (RNN-LSTM) and Mobile Net.
Logistic Chaos-Based Wild Horse Optimization for Enhanced Color Image Segmentation
Color image segmentation is a crucial step in various image processing applications, playing a vital role in understanding image content. Nature-inspired optimization algorithms have demonstrated significant potential in enhancing segmentation performance. This paper proposes a novel color image segmentation method based on Wild Horse Optimization, enhanced with Chaos Theory, to avoid local optima and accelerate convergence. A Logistic map is integrated into the Wild Horse Optimization to ensure diverse population initialization and to introduce dynamic perturbations during the optimization process. The proposed method is evaluated on various color images. Metrics such as Mean Square Error, Peak Signal-to-Noise Ratio, and Structural Similarity Index are employed to assess the quality of the segmented images. Additionally, the best fitness values, computed using the Davies-Bouldin Index, are analyzed to demonstrate the algorithm’s capacity to achieve optimal segmentation solutions. Comparative experiments with other metaheuristic segmentation methods confirm the superior effectiveness of the proposed Logistic Chaos-based Wild Horse Optimization approach.
Enhancing Short-Film Visual Storytelling with AI-Generated Attention and Focus Maps: A Dual-Map Framework for Data-Driven Analysis
Visual storytelling in short films relies heavily on directing audience attention, yet traditional methods for analyzing visual saliency often lack cinematic context and temporal dynamics. We propose a dual-map framework merging AI-generated attention heatmaps with focus maps to deliver data-driven insights for visual storytelling. The attention heatmap model, trained on cinematic datasets, identifies salient regions while the focus map model, which includes temporal attention gates, captures dynamic shifts in viewer focus. The framework employs spatial clustering to localize coherent areas of interest and quantifies the consistency between predicted attention and actual focus patterns. Furthermore, the system produces annotations that are compatible with standard editing software, which permits filmmakers to improve their work based on empirical evidence. The proposed method fills a crucial void in computational film analysis by merging static saliency with temporal attention modeling, thus creating a tool which connects artistic intent and audience perception. Experimental findings show that the framework successfully identifies gaps between intended and actual viewing behaviors, yielding practical insights for optimizing visual storytelling. This study advances both film studies and human-computer interaction by introducing a scalable, interpretable method for examining and improving cinematic narratives. Merging attention and focus maps creates novel opportunities for data-driven filmmaking, enabling artistic choices to be guided by empirical visual assessment.
ADAPT: General-to-Specific ADAptive Prior Transfer for Hyperspectral Image Reconstruction
Hyperspectral imaging (HSI) captures rich spectral details far beyond RGB cameras. To enable real-time acquisition, contemporary HSI systems often rely on compressive sensing theory, making the reconstruction of hyperspectral data from compressed measurements a fundamental and challenging task in computational imaging. While supervised deep learning has achieved remarkable success in hyperspectral (HS) image reconstruction tasks, the generalization capability of these existing models is often hindered by limited training data, resulting in poor performance on unseen scenes. To address this issue, we propose ADAPT (ADAptive Prior Transfer), a test-time prior transfer framework that bridges the gap between the general priors learned by a pre-trained model and the specific information of a target scene. Our method leverages a pre-trained deep unfolding network and test-time fine-tuning using a loss function derived from the physical CASSI imaging forward model and the emprical prior of HSI. Experiments on the KAIST dataset demonstrated that our method improved the reconstruction quality, boosting the average PSNR from 33.24 to 34.80 dB.
Clustering Websites by Salient Design Features
Designing websites to meet client expectations often involves repeated refinement cycles, which can be both time-consuming and resource-intensive. To address these challenges, this paper presents a versatile, automatic classification system for categorizing real-world websites based on their salient design features, with the goal of supporting automatic website generation. Leveraging Self-Organizing Maps (SOMs) and computer vision techniques, we analyzed website wireframes extracted through a pipeline of image processing techniques, including edge detection, morphological filtering, gradient analysis, and image smoothing. Experiments were conducted on three datasets: manually created wireframes, screen captures of the top 100 websites, and screen captures of the top 1000 websites ranked by SimilarWeb. The analysis revealed seven distinct clusters of website designs: dashboard interfaces, simple information pages, fixed-width product grids, informational pages with sidebars, basic search interfaces, multi-section content layouts, and tabular data interfaces. The classification system successfully grouped websites with similar structural patterns and design features. This research demonstrates that wireframe lines are fundamental elements useful for classification of website designs and that SOM is an effective approach for detecting clusters in website classification. The resulting classification system can provide support for automatic website generation and template creation, potentially reducing the iterative burden of the web design process.
Comparative Study of Quasi-random Sampling for Brain Tumor Segmentation in MRI Using Expectation-Maximization (EM) and Region Growing
Brain tumor detection remains a challenging problem due to the complexity of tumor structures and variability in Magnetic Resonance Imaging (MRI) data. Accurate detection and segmentation of brain tumors in medical imaging are essential for effective diagnosis and treatment planning. This study presents a multi-segmentation approach for brain tumor localization combining the Expectation-Maximization (EM) algorithm with an improved region-growing classification technique. To improve segmentation accuracy and computational efficiency, several quasi-random sampling methods were evaluated, including Halton, Sobol, Hammersley, Faure, and Poisson Disk Sampling. Experimental results demonstrate that the Sobol sampling method achieves superior precision in delineating tumor and edema regions. Its robustness and efficiency make it particularly suitable for complex medical imaging applications. Although other sampling techniques also produced promising results, Sobol consistently outperforms them in both speed and accuracy. In conclusion, the study highlights the importance of adapting segmentation strategies to the dataset characteristics, clinical requirements, and operator expertise to ensure optimal performance in real-world implementation.
Multi-view Integration with View-specific Self-attention for Sign Language Recognition
Sign Language Recognition (SLR) plays a pivotal role in mitigating communication barriers between the deaf community and broader society. Recently, the Video Transformer Network (VTN), an extension of the transformer architecture incorporating a multi-head self-attention mechanism, has demonstrated efficacy in video processing tasks and SLR. However, relying solely on Red Green Blue (RGB)frame information may render the model susceptible to redundant data and environmental factors such as lighting variations and complex backgrounds. Additionally, most existing SLR datasets provide only frontal viewpoints, which constrain the model’s generalizability, particularly in real-world settings where diverse perspectives are prevalent. In this study, we propose Video Transformer Network with 3 Graph Convolutional Network(VTN3GCN), a multi-view and multi-stream framework that integrates RGB data, skeleton coordinates, and pose flow from three distinct viewpoints: left, right, and center. This architecture enhances VTN by incorporating Graph Convolutional Networks (GCN) to learn skeletal frame features and employs an early fusion mechanism between the RGB and skeleton streams. Experiments conducted on the Multi-VSL200 dataset—a newly curated dataset for Vietnamese Sign Language (VSL) featuring three viewpoints per video demonstrate that the VTN3GCN framework achieves a top-1 accuracy of up to 92.92%, achieving state-of-the-art accuracy. Our code and data are available at: https://github.com/fossbk/MultiView-ISLR/tree/main/VTN3 GCN.
Privacy-preserving Simulation of Facial Palsy Expressions Using Diffusion Models
Facial nerve palsy results in impaired voluntary facial movements, and in Japan its severity is commonly assessed using the 40-point Yanagihara method. However, visual assessment is prone to inter-rater variability, and the use of actual patient images for educational purposes is restricted due to privacy concerns. To overcome these limitations, we propose a simulation framework that produces synthetic facial palsy images by leveraging a Stable Diffusion model with a partially fine-tuned ControlNet. Fine-tuning is confined to timesteps representing higher-level features, which allows the generation of pathological expressions while reducing the likelihood of reproducing patient-specific identity. Furthermore, a preprocessing stage modifies the size and placement of facial components and incorporates contour information from other faces. This design helps keep the fidelity of simulated expressions while maintaining strong privacy protection. The proposed method offers a practical resource for medical training and clinical research, where realistic and privacy-preserving facial data are required.