Journal of Image and Graphics aims to publish original, previously unpublished, research, reviews, communication, plus case studies and short research notes, on both applied and theoretical aspects in imaging and graphics research.
Blood Cancer Classification via Attention-Guided Deep Networks on Microscopic Smear Images
Blood cancer is a type of cancer which has affected many people and their lives have flipped totally while battling the cancer. Most common type of blood cancer is Acute Lymphocytic Leukemia (ALL), Acute Myeloid Leukemia (AML) and Multiple Myeloma (MM). One of primary tests done by pathologist to confirm the diagnosis is microscopic blood smear analysis, which requires human interpretation and consumes a lot of time. To overcome these problems, this proposed study helps them with a distinctive and a novel automated neural network mechanism, called HemoNet, which is a CNN architecture with a CBAM mechanism, incorporated to correctly identify the type of cancer within less time. This study poses as a helping hand rather than completely taking over the human interpretation. The work has been performed over ALL, AML, MM blood smear image dataset. Data transformation was performed to overcome the overfitting, allowing the model to learn the features with different characteristics. The baseline model ‘DeepLeukNet’ which when trained on the dataset, obtained an accuracy of 99.56% with a validation loss of 0.4. Whereas the proposed HemoNet model which has CBAM module, obtained an accuracy of 99.56% with a validation loss of 0.34, and about 79% faster.
Topic: Machine Learning Based Techniques for Image and Video Processing
Copyright (c) 2025 Journal of Image and Graphics
Detection of Ergonomic Sitting Postures in Office Environments
Prolonged sitting in office environments increases the risk of musculoskeletal disorders and related health issues. To mitigate these risks, this study develops an automated system for classifying ergonomic and non-ergonomic sitting postures using image-based analysis with machine learning. A dataset of 1,800 instances from 30 participants in controlled settings was used, featuring both ergonomic and non-ergonomic postures. The MoveNet model extracted key points (head, shoulders, elbows, spine) from images, which were normalized to adjust for body size and position. Three machine learning algorithms—AdaBoost, XGBoost, and Multi-layer Perceptron (MLP)—were applied for classification. AdaBoost achieved the highest accuracy (91.93%), precision (95.87%), and F1 score (90.27%), while XGBoost had the best recall (86.03%) and AUC (97.5%). Results indicate that the model effectively classifies postures across multiple angles and outperforms existing approaches. This system shows promise for real-time ergonomic monitoring in office environments, potentially reducing musculoskeletal risks and improving workplace health. Future research will focus on expanding datasets to improve generalizability across diverse postures and settings.
Dense-SVM: An Intelligent Model for Pancreatic Cancer Detection
Pancreatic cancer (PC) is a very typical to treate and cure among different type of cancers, which is often found too late and spreads quickly, making survival rates very low. Imaging modalities has mostly been used to diagnose pancreatic cancer, but the most up-to-date imaging gives a bad outlook, which limits the treatments doctors can use. By combining deep learning and machine learning, doctors can make better decisions and find cancer earlier. In this paper, DesnseNet 201 a deep learning model and Support vector machine (SVM) classifier is utilized for identification of pancreatic cancer (PC). Three stage processing of input data is performed to achieve the results. First stage is data pre-processing; second stage is extraction of features using principal component analysis (PCA) and DenseNet 201 model and final stage is classification using SVM model. The most significant features from the given input image is been extracted using the process of PCA and DenseNet 201 model. The parameters like accuracy, precision, recall, Specificity and F measure are evaluated. The accuracy obtained using the proposed three stage detection model is 94.52% and is better compared to traditional model.
Advancements in Image Inpainting: A Deep Learning and GAN-Based Perspective with Defined Research Objectives
Image inpainting is an important task in computer vision with wide-ranging tasks, including photo restoration, object removal, and medical imaging. Traditional techniques often fail in preserving semantic understanding and structural consistency in complex images. Along with the emergence of machine learning, especially Generative Adversarial Networks (GANs), image inpainting has achieved remarkable progress. This paper provides in depth analysis of image inpainting techniques, recent progress using machine learning and GANs. Furthermore, the paper proposes a novel research framework that aims to improve perceptual quality, structural coherence, and context-aware filling in inpainting tasks. Experimental comparisons showcase the improvement of the suggested approach over futuristic models.
A Deep Learning Approach to Enhance Breast Cancer Diagnosis Accuracy through Ultrasound Image Segmentation
This study presents a deep learning-based approach to enhance the accuracy of breast cancer diagnosis through ultrasound image segmentation. Several segmentation models were evaluated, including the newly developed BCARE-FEZNET, alongside U-Net, FCN, ResUnet, SegNet, and Mask R-CNN. The evaluation criteria included accuracy, loss, and an in-depth analysis using metrics such as Confusion Matrix, Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Area Under Curve (AUC). The results demonstrate that BCARE-FEZNET outperforms the other models, achieving approximately 92% accuracy and an AUC of 0.56, indicating a well-balanced trade-off between precision and recall. While ResUnet delivers the highest AUC (0.76), it suffers from significant imbalances between object detection and false positives. Other models, such as U-Net, FCN, and Mask R-CNN, exhibit AUC values close to random guessing, while SegNet encounters instability during training. Overall, BCARE-FEZNET provides the most stable and reliable performance, proving to be the optimal model for ultrasound image segmentation in breast cancer diagnosis.
Vision Mamba-Based Dual-Phase Self-Supervised Framework for Neonatal Jaundice Diagnosis
Neonatal jaundice is a common and potentially serious condition that, if left undiagnosed or untreated, can lead to severe neurological complications in newborns. Existing diagnostic methods are often invasive and face limitations in accuracy, accessibility, and data availability, especially in resource-constrained environments. This study introduces NeoViM, an adapted MambaVision-based framework for neonatal jaundice classification. The framework adopts a two-stage approach: In the first stage, the adapted MambaVision is used as a deep feature extractor. Self-Relative Clustering (SRC) is then applied to learn discriminative features by organizing images into clinically meaningful clusters, enabling effective unsupervised learning from limited data. To further enhance feature quality, a self-supervised learning strategy based on Linear Kernel Centered Alignment (LCKA) loss is employed to refine the extracted representations. In the second stage, the pre-trained model is fine-tuned on a small labeled dataset, allowing the system to adapt the learned features for accurate jaundice classification. The methodology was evaluated using the publicly available NJN dataset and achieved a classification accuracy of 93.42% and an F1 score of 93.37%, outperforming previous methods applied to the same dataset. This two-step framework ensures high diagnostic performance while maintaining scalability and accessibility across diverse clinical settings.
An Improved Method for Repeatable Data Hiding
Repeatable data hiding enables multiple embedding operations on digital images without accumulating distortion. However, all the LSBs (Least Significant Bits) of pixels are set as zero for embedding costs assignment in existing frameworks, which leads to imprecision of the embedding cost, thereby reducing imperceptibility and security. This paper proposes an improved method that achieves distortion invariance without full LSB-zeroing, enhancing security while preserving repeatability. In our method, a part of LSBs is reset to zero, generating a partial LSB-zeroed image that retains the original LSB values of non-modifiable pixels. By ranking pixels based on embedding costs derived from the partial LSB-zeroed image, our method ensures the same set of pixels is consistently selected as modifiable pixels across multiple embeddings. Therefore, the modification-trace of the current embedding is replaced by the latter embedding, guaranteeing repeatability of data hiding.
Study of 2-D Coordinate Calibration Method for Machine Vision Systems using Regression Analysis
This paper aims to investigate the coordinate calibration method in two dimensions (2-D), specifically quadratic transformation, for machine vision systems integrated with robot arms. This method establishes the relationship between image coordinates and the world coordinate system, allowing the data from the world coordinate system to be utilized for robot arm manipulation. The relationship between image coordinates and the world coordinate system was established using a calibration paper containing 13 black circles in the X-direction and 12 black circles in the Y-direction, each with a radius of 10 mm. The distances between the centers of circles are 22 mm and 16 mm in the X-direction and Y-direction, respectively. Image processing and machine vision methods were employed to determine the image coordinates of the calibration points. The results of this study demonstrate that the coordinate calibration method is effective and accurate in determining the position of objects in space for robot arm manipulation.
An Approach to the Problem of Calculating Target Scores Using Camera to Support Shooting Training
Image processing methods are now widely used in the world in civilian fields such as handwriting recognition, face recognition, gender recognition, self-driving cars, disease diagnosis, etc. The article introduces a target reporting method using machine learning algorithms and image processing methods to identify bullet marks on targets, supporting shooting training. Bullet mark images taken by camera will be digitized and the K-nearest neighbor machine learning recognition algorithm will be applied. The algorithm models after being trained give quite accurate results in identifying bullet mark locations in the target mark circle, thereby calculating the total score achieved by the shooter after shooting, contributing to improving shooting training capacity.
Deep Learning Model for Automated Heart Disease Diagnosis Using ECG Signal
The development of technology improves the early detection of disease and diagnosis in the field of medical. Most of the people suffer with heart disease depending on the age factor and is millions of death cases are recorded due to heart attacks. In this paper heart rate variability (HRV) is measured for early detection of cardio vascular disease (CVD). HRV analyse the heartbeats and evaluated the time intervals between the consecutive heart beats. The CPSC 2018 dataset is considered in this paper to evaluate the ECG signals. Initially the R-R intervals are evaluated. The key features of the HRV like spatial, frequency, Poincare are extracted and a deep learning model Efficient-Net b0 (ENb0) is used for classification and identification of heart disease. The ROC curve and confusion matrix is achieved using the deep learning classification model. The parameters like accuracy, precision, recall and F1 score are evaluated using the proposed deep learning model and compared with other techniques. The ENb0 model achieves better efficiency in parameters evaluated and is effective in early detection of CVD and helps to support the research in the field of medical.
Binomial Dropout Convolutional Neural Network Classifier-Based Micro-Expression Recognition System
Facial expression recognition is essential for a variety of applications, including marketing, commerce, security systems, and psychotherapy. The two primary categories of facial expressions are macro-expression and micro-expression. When comparing macro expression, it can be difficult to recognize micro expression in real time because it typically happens in high stakes scenarios. So, in this work, auto-detection of facial micro expressions was proposed by using a Binomial Dropout Convolution Neural Network (BDCNN) classifier technique. Initially, the input video collected from the publicly available source is converted into frames and the keyframes are generated by using the Distance Deviation-based Rood Pattern Search algorithm (DDRPS). Then, the keyframes are preprocessed to reduce the significance of illumination, face detection, landmark points on the face, and motion magnification. Afterward, the important features are extracted from the preprocessed frames. Finally, the BDCNN classifier was used to recognize the micro-expression. The average accuracy and F- Measure values obtained on the SAMM data set are 0.9758 and 0.9247; also, for the SMIC dataset, the values are 0.8989 and 0.8536, similarly, for the CASME II dataset, the accuracy and F-Measures are 0.9784 and 0.9509. The proposed classifier is compared to state-of-art methods to prove its superiority.
CLDS-YOLO: Corn Leaf Disease Detection and Severity Evaluation Using YOLOv9
Corn is one of the main crops around the world, especially in the Philippines, where agriculture is threatened by diseases such as Northern Corn Leaf Blight, Gray Leaf Spot, and Corn Rust. Tropical cyclones and environmental variables worsen these illnesses, resulting in dramatically lower crop yields and quality. This study introduces CLDS-YOLO (Corn Leaf Disease Detection and Severity Evaluation Using YOLOv9), which utilizes YOLOv9 instance segmentation for high-precision disease detection and integrates fuzzy logic for severity evaluation with relative leaf area (RLA) computation and the number of diseased regions. YOLOv9e-seg achieved near-perfect precision for leaf detection (approximately 1.0), but showed challenges in identifying diseased regions, with lower precision ~0.7 and recall ~0.3 for segmentation tasks. Despite these limitations, the model's boxing tasks demonstrated higher performance, reflected in mAP@0.5 and F1-scores. Severity analysis successfully classified disease levels, facilitating effective crop management. This study highlights the potential of CLDS-YOLO in real-time disease detection and severity evaluation, offering a foundation for an indoor planting system that ensures consistent crop health monitoring and protection from adverse weather conditions.
A Novel Algorithm for Sprial Large FOV Reconstruction
Spiral CT can extend the longitudinal field of view (FOV), but its transverse field of view is still limited. To address the issue of limited transverse FOV, scanning scheme of spiral offset is designed to maximize the imaging field by a factor of 1. To compensate for the transverse truncation in projection, the redundancy property of projection data is utilized, and Parker weights is employed to achieve high-quality reconstruction of truncated data. For the reconstruction algorithm of spiral offset, the performance of the algorithm is tested using simulation phantoms and real data, with peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean square error (MSE) as quality evaluation metrics, based on the standard of images reconstructed from conventional scanning and reconstruction. The experiments demonstrate that the images reconstructed based on the proposed method have high quality and can faithfully reconstruct the details.
CNN-Canny-Based Signature Scrutiny Verification Techniques in Banking Applications
Ensuring the security of financial transactions and protecting official signatures on financial documents have always been a priority for individuals and international banks. Bank checks play a crucial role in global financial exchanges, with a vast number processed daily. However, signature forgery poses significant risks, leading to identity theft, financial losses, and reputational damage for both financial institutions and customers. Fraudulent transactions impact a bank's financial stability and erode consumer trust. Therefore, there is an urgent need for innovative solutions to enhance security and prevent signature-related fraud. This study proposes an automated signature verification system for bank checks, leveraging advancements in image processing and deep learning. Our approach begins with the acquisition of a customer's handwritten signature from check leaves. The core innovation of our methodology lies in integrating the Canny edge detection filter within the convolutional network layers, replacing conventional CNN filters. This hybrid approach enhances feature extraction by focusing on critical signature contours, improving classification accuracy. Experimental results demonstrate that the proposed CNN-Canny hybrid model achieves 98.8% accuracy, outperforming traditional CNN-based methods, which achieved 95.1% accuracy. These findings highlight the potential of edge-aware deep learning techniques in strengthening security measures.
Image/video quality assessment challenges: A comprehensive review
Lossy coding schemes which generates distortions in the images/videos are used by video encoders to meet the bandwidth requirements. Similarly, preprocessing, transmission, and post processing steps generates additional artifacts. Measuring these artifacts is very crucial for digital video systems developments. However, there are many issues/challenges while measuring quality of an image/video efficiently and effectively. Many existing research papers present the issues/challenges while doing quality assessment. However, existing work do not present all the issues/challenges comprehensively and discusses either one or two kinds of issues during quality assessment. However, this work presents a comprehensive overview of all kinds of issues & challenges while measuring image/video quality, i.e., ranging from subjective to objective, using 3D video & new coding tools, effect of one type of distortion to another types of distortion, issues due to subjective databases, etc. The paper also describes issues/challenges related to quality of experience (QoE) by the end users. The work also discusses other miscellaneous issues/challenges and provides recommendation for future directions based on the current work understanding. Overall, this work will guide the research community to take into account these issues and develop more efficient ways of measuring image/video quality along with developing efficient encoders and 3D encoding methods for the further development of digital video systems.
WGEF: An Optimized Deep Learning Model for Recognition of Road Surface Condition Using SVM Classifier
Road surface often plays a crucial role in day-to-day scenario and a major source of transportation. The dependency of public, government, and other applications on road is major issue. The roads frequently get damaged due to the weather conditions. Different weather conditions play different role on the road surface. Identification of road conditions is getting difficult. To overcome the problem, in this paper proposed a deep learning (DL), Optimization and machine learning (ML) model and combine both the models for recognition and classification of Road surface conditions (RSC). The model is designed on publicly available dataset. The images in the dataset are processed using wavelet transform (W) and extracted GLCM (G) data points. The detailed feature extraction is performed using Efficient-Net (E) model. The obtained data points are optimized using firefly optimization (F) and finally classified using support vector machine (SVM). The identification of RSC is performed using low number of data points and achieved best result. The combination of DLML provided better results in term of metric evaluated like sensitivity, specificity, precision, and accuracy. The rate of accuracy achieved using the proposed design is 98.15%.
Performance Evaluation of Different Optimizers on Alzheimer’s Disease Classification using a Customized Convolutional Neural Network
Alzheimer's disease is a type of dementia that usually affects elderly people. It is a neurological disorder that causes a patient to lose memory gradually over time. The brain of an Alzheimer disease (AD) patient shrinks due to the accumulation of amyloid plaques in between the neurons. As a result, the neurons, which are the basic building blocks of the brain, lose connections and cannot communicate with each other. A person can be prevented from having AD if diagnosed at the right time. So it’s very important to detect patients with mild symptoms of dementia in order to save them from getting AD. In this work, we have proposed a customized CNN model for classifying Alzheimer’s disease. The model has been evaluated with two benchmark datasets, the Kaggle Alzheimer’s dataset and the ADNI dataset. The two datasets differ in the number of images. The K-fold technique has been applied to overcome the problem of class imbalance. We have updated the model parameters using optimizers, namely SGD, SGD with momentum, AdaGrad, AdaDelta, RMSprop, and Adam. Experimental results established that the proposed model outperforms many of the state-of-the-art models, considering the two benchmark datasets.
A Comparative Study of Convolutional Neural Networks (CNN) Architectures on Microscopic Blood Film Images for Malaria Diagnosis
Malaria has been recorded as one of the deadliest diseases globally. Accurate diagnosis is essential for suitable treatment, and the traditional practice of malaria diagnosis has proved inefficient as results depend on the skills of the health personnel. In recent times, deep learning models have proved helpful in the quick detection of malarial parasites. This research focused on developing a classification model of Convolutional Neural Networks (CNN) architectures and comparing these models to identify the most effective one for automatic malaria parasite detection on thin blood smear images. A dataset of 27,558 digital blood images was collected from the National Institutes of Health (NIH) database in Bangkok, Thailand. The dataset was categorized into parasitized and uninfected cells and was fragmented into training (80%) and validation (20%) sets. Performance metrics for measuring the model's performance include sensitivity, specificity, precision, and F1 Score. The model predicted and classified thin blood smear digital images as either parasitized or uninfected with custom InceptionV3 outperforming the VGG19 and custom CNN with an accuracy of 89.85%. The result shows that malaria diagnosis on microscopic thin blood images using deep learning can potentially improve early detection of malaria parasites, which could prevent deaths, reduce the workload of parasitologists, and eliminate other limitations of the traditional malaria diagnostic approaches.
Robotic Arm: Cutting Edge Sorting Machine for Industrial Optimization
This paper introduces an innovative Sorting Robotic Arm (SRA) utilizing a SCARA (Selective Compliance Assembly Robot Arm) model for hardware and imposing the advanced object detection capabilities of the Grounding Dino model, enabling real-time identification and sorting of objects with exceptional accuracy and speed. The modular design of this arm facilitates easy customization and scalability, allowing for seamless deployment in various industrial settings. By harnessing the capabilities of SCARA robotics and advanced AI. This paper aims to revolutionize sorting processes, improving productivity, accuracy, and efficiency in industrial operations including recycling facilities, logistics and warehousing, seed sorting in agriculture.
Special Issue: Advancements in AI-Driven Image Processing and Graphics: Techniques, Applications, and Challenges
Performance Comparison Analysis of Random Forest, Support Vector Machine, and AdaBoost in Arrhythmia Classification
Arrhythmia is a condition characterized by irregularities in heart rhythm, where the heartbeat may be too fast, too slow, or irregular, potentially leading to severe complications such as heart attacks or sudden cardiac death. Accurate diagnosis of Arrhythmia is essential but traditionally relies on Electrocardiogram (ECG) analysis by medical experts, which can be time-consuming and subject to variability. In recent years, computational methods have gained prominence in arrhythmia classification, improving diagnostic speed, Accuracy, and automation. This study investigates the effectiveness of machine learning models, namely Random Forest (RF), Support Vector Machine (SVM), and Adaptive Boosting (AdaBoost), in classifying arrhythmias using features extracted from ECG signals through Discrete Wavelet Transform (DWT). The dataset is sourced from the MIT-BIH Arrhythmia Database, and the research involves several stages, including data collection, preprocessing, feature extraction, model training, and performance evaluation. The results indicate that Random Forest achieves the highest Accuracy at 97.6%, Support Vector Machine at 97.2%, and AdaBoost at 90.2%. These findings demonstrate the superior performance of Random Forest in handling arrhythmia classification tasks, highlighting its potential for enhancing automated ECG interpretation and assisting in early diagnosis and clinical decision-making.
Optimized Heart Disease Image Classification on Edge Devices using Knowledge Distillation and Layer Compression
The high number of edge devices enables remote, real-time diagnostic testing, particularly in remote areas. Convolutional neural network models (CNNs), in particular, are excellent at analyzing CT scans and other medical images and CXRs for heart diseases, but their computational complexity usually makes them unsuitable for usage on edge devices with limited resources. This paper presents a new compressed layered knowledge distillation model for precise medical image diagnosis, for example, SARS-COVID-19 and other heart diseases. We utilize knowledge distillation to transfer the teacher network's knowledge to a smaller, compressed student network for edge deployment. Moreover, we utilize a well-structured layer compression approach, with a focus on decoupling and merging and not pruning, to optimize the student network architecture. Two data sets, Chest CT-Scan and SARS-CoV-2 CT-Scan, were used to test the suggested model. When compared to the existing models, our performance is superior in the following ways: 98.93%, precision 98.41%, recall 98.69%, F1-score 98.44%, MSE 0.04, RMSE 0.16 for the CT-Scan data set and SARS-CoV-2 and 98.25%, precision 98.78%, recall 98.86%, F1-score 98.14%, MSE 0.09, RMSE 0.13 for the Chest CT-Scan data set. These results verify the efficacy of our intended technique for achieving high diagnostic accuracy at low error in edge devices.
Comprehensive Analysis of Feature Detectors and Descriptors for Stitching UAV Images with Perspective Distortion
This study analyzes traditional and deep-learning-based feature detectors and descriptors for UAV image stitching under perspective distortion. Traditional methods, including SIFT, SURF, and AKAZE, achieve high keypoint detection and matching accuracy but struggle with geometric distortions, leading to artifacts such as ghosting and misalignment in complex UAV scenes. Deep-learning-based approaches, such as SuperPoint, ALIKED, and DISK, offer superior alignment accuracy and visual coherence by detecting fewer but more robust keypoints. DISK and ALIKED, in particular, demonstrate high spatial consistency, reducing perceptual artifacts as validated by PIQE, NIQE, and BRISQUE metrics. The results indicate that deep-learning-based detectors outperform traditional methods in UAV image stitching under perspective distortions. Future work will explore hybrid models that combine the strengths of both approaches to enhance stitching accuracy and computational efficiency.
CenterFormer: Coupling CenterNet and Vision Transformer for Accurate Wheat Head Detection
Wheat is a staple crop cultivated widely across the world, making effective management of wheat fields a critical task. A key component of this management is accurately identifying and counting wheat heads, which provides essential data for assessing growth conditions, estimating crop yields and optimizing agricultural. This study introduces a novel approach for automatic wheat head detection by treating the wheat head as a single point to avoid ambiguous annotation of dense objects while leveraging the long-range dependency modeling capabilities of Transformer architecture to learn multi-scale features for head prediction, dubbed as CenterFormer. Specifically, we employ a hierarchical Transformer architecture with self-attention exploitation in both spatial and channel domains as the backbone to extract multi-scale features in the hierarchical stages. To maintain the linear complexity of the Transformer block, we implement window-based self-attention in spatial domain and group-wised self-attention in channel direction. In addition, to leverage the multi-scale features with both detailed spatial information and abstracted semantic contexts, we design a simple yet effective fusion block to integrate these features for enhanced wheat prediction. The prediction block aims to estimate a heat map, denoting the probabilities if the points are located at the centers of the wheat heads, and regresses other object properties such as size and sub-pixel deviations for each center location. Extensive experiments on the Global Wheat Head Detection (GWHD) dataset have demonstrated that our proposed method achieves substantial performance improvements compared with the state-of-the-art object detection models.
Fine-Tuned Object Detection for Mask Recognition Using Green Computing in IoT Systems
This study proposes a sustainable and intelligent approach to face mask detection, a critical component for ensuring public transportation safety within IoT environments. By fine-tuning an object recognition model, the system is designed to accurately detect individuals wearing face masks using energy-efficient, or "green," computational methods. The research leverages a comprehensive dataset on road accidents and vehicle information, analyzing factors such as fatalities, vehicle movement patterns, driver gender, trip purposes, road types, and days of the week. Advanced data analysis and visualization tools—including bar charts and pie charts—are employed to highlight significant patterns and insights. A key aspect of the study involves exploring the correlation between accident occurrences and vehicle manufacturers, aiming to uncover potential causative relationships. The integration of green computing techniques ensures efficient handling and processing of large-scale IoT data while optimizing the detection model’s accuracy and performance. This research contributes to the broader domain of Advancements in AI-driven Image Processing and Graphics: Techniques, Applications, and Challenges, particularly in the context of public safety and smart transportation systems. The proposed face mask recognition model is trained on relevant datasets and optimized to perform reliably in real-world scenarios. The findings not only emphasize the importance of monitoring passenger mask usage but also provide actionable insights for transport authorities and policymakers. By analyzing trends and behavioral patterns, this work supports the development of more effective safety regulations and promotes the integration of AI-driven, sustainable solutions into IoT-based public transportation systems.
Performance Comparison of YOLOv8, YOLOv5 and YOLOv11 in Nature Image Segmentation
Image Segmentation is one of the important tasks in computer vision techniques. This technique is a basic technique for partitioning image into detailed segments that enable the analysis and retrieval process. This paper discusses the performance of the three YOLO models, especially YOLOv5, YOLOv8, YOLOv11 in nature image segmentation by focusing on reptile images. Of course, with a diverse dataset of reptile images taken under different environmental conditions, this experiment accuracy, precision, recall, Mean Average Precision (mAP), and Computational Efficiency. From the experiments carried out, we carried out comparative tests related to the three models and produced different outputs. These three models of course also have one of their advantages, which shows the best performance of each model. YOLOv5 is a model that is easy to implement, YOLOv8 can approach without using anchors, and YOLOv11 has a higher efficiency aspect than the two models. The final results show that YOLOv11 introduced significant improvements in architecture and training methods, making it a versatile choice for a variety of computer vision tasks.
Efficient Few-Shot Image Classification with Unbalanced Sinkhorn Distance for Robust Feature Alignment and Large-Scale Applications
The core challenge of few-shot image classification lies in efficiently learning and accurately classifying with limited labeled data. Current metric-based meta-learning methods primarily rely on computing the structural distance between the query and support sets for matching. However, traditional metric learning typically employs Euclidean distance or standard Sinkhorn distance for feature matching, assuming that the total mass of the source and target distributions is equal. This assumption overlooks the imbalance in data distribution in real-world tasks, leading to reduced matching efficiency. Additionally, intra-class feature distribution imbalance can further impact the accuracy of metric learning. To overcome these challenges, this study systematically introduces Unbalanced Sinkhorn Distance (USD) into few-shot learning to enhance the model’s ability to match features under imbalanced distributions. USD allows dynamic adjustment of matching distributions during metric computation, making it more adaptable to real-world data imbalances. Moreover, this method effectively reduces the interference of background noise when matching features between the support and query sets while maintaining low computational costs, thereby improving both classification accuracy and efficiency. Experimental results demonstrate that our method achieves state-of-the-art performance on four few-shot learning benchmark datasets: Mini-ImageNet, Tiered-ImageNet, FC100, and CUB, significantly outperforming existing optimal transport (OT)-based methods.
Evaluating Facial Emotional Proportion Based on Computer Vision Technique
Emotion detection is a technique to recognize human emotions by addressing facial expressions. It is essential for psychology, security systems, and human-computer interaction. The ability to perceive and interpret an individual’s facial expressions helps to understand their actions and improve the interaction between a person and a computer. Facial emotion recognition (FER) is instrumental whenever there is a need for human-computer interaction for behavioral assessment, like in clinical usage. When using machine learning models in the Face Emotion Recognition (FER) field, the accuracy and robustness remain difficult because of the diversity of human faces and image changes, such as differences in spatial pose and lighting. This research used the FER2013 dataset, which contained approximately 30,000 images divided into seven classes (anger face, disgust face, fear face, happy face, sad face, surprise face, and neutral face). It also used two CNN models (VGG19 and Sequential). The result of the VGG19 model achieved 67% accuracy, validation accuracy achieved 64%, the sequential model achieved 77% accuracy, and validation accuracy achieved 66%.
Vision Transformer for the Categorization of Breast Cancer from H&E Histopathology Images
Breast cancer (BC) is the most frequent form of cancer, accounting for 24.5% of all cancer cases worldwide, with projections estimating 364,000 cases by 2040.Accurate diagnosis and effective categorization of BC are essential for proper treatment planning, patient management and improved survival.Traditionally, pathologists examine histopathology specimens manually using a microscope to categorize the BC, which is labor-intensive, time-consuming, prone to subjectivity, and constrained by the availability of experts. An automated approach can address these limitations, but previous automated methods failed to achieve sufficient accuracy and reliability. This study presents an automated BC categorization method leveraging whole slide histopathology images and a transformer-based deep learning model. The proposed method uses a cascade of transformers to classify BC using 40X histopathology images, following the taxonomy defined by the BRACS dataset, distinguishing between benign, atypical, and malignant cases. First, it classifies BC into three primary categories—benign, atypical and malignant—and subsequently determines the specific sub-types within each category. The proposed method was validated using two widely recognized datasets: BRACS and BreakHis. On BRACS, it achieved 95.6% accuracy in classifying BC into benign, atypical, and malignant categories, with sub-type accuracies of 94.7% for benign, 98.6% for atypical, and 99.1% for malignant cases. On the BreakHis dataset, the model achieved 93% accuracy for binary benign-malignant classification, with sub-type accuracies of 94% and 91% for benign and malignant cases, respectively. Proposed method outperformed existing methods in accuracy and robustness, making it a promising tool for automated BC diagnosis and classification.
Evaluation Study for the Performance of PSO and AOPSO Algorithms in Optimizing the SVM Classifier
Face recognition plays a crucial role in our daily lives by identifying and authenticating goals. One of the most common face recognition approaches is the support vector machine, which is a supervised machine learning method. The present face recognition methods have utilized PSO and AOPSO approaches for optimizing the training parameters in SVM. This paper is a comparative study of PSO and AOPSO algorithms that are used as a face recognition model with SVM (PSO-SVM and AOPSO-SVM). The suggested methods are implemented and evaluated on two human faces datasets: CASIA V5 and FEI Face datasets. The proposed methods are applied for ten rounds using n-fold cross-validation. Three different metrics are used to evaluate the PSO and AOPSO algorithms and assess their performance: accuracy results, computational time and to assess their optimization through two optimization functions. The overall comparison results demonstrate that the AOPSO outperforms the standard PSO algorithm in terms of accuracy and computational time for CASIAV5 and FEI Face datasets. Moreover, the AOPSO is better than the standard PSO algorithm for most rounds using the optimization functions based on their fitness values.
Facial Expressions in Virtual Reality Based-Education: Understanding Recognition Approaches and their Integration for Immersive Experiences
The integration of virtual reality (VR) into teaching has become a crucial strategy to address the challenges of education during times of crisis. This article explores the role and significance of facial expressions in education and investigates the potential for integrating them into virtual reality environments. We outline the approaches employed in this field and present their integration in a VR application designed to simulate a classroom setting. Within this virtual classroom, an avatar has been implemented to replicate the teacher’s facial expressions using the techniques discussed in this research. Our proposed solution highlights the importance of VR integration and its effectiveness in teaching, particularly in distance learning contexts. Additionally, this approach offers a cost-effective alternative to other expensive solutions, making it more accessible to a wider audience.