The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Computer vision is the field of artificial intelligence concerned with enabling computers to interpret and extract information from visual data such as images and video. Core tasks include image classification, object detection and localization, semantic and instance segmentation, facial recognition, and optical character recognition. Early approaches relied on hand-crafted feature descriptors combined with classical machine learning classifiers; current computer vision is dominated by convolutional neural networks and, increasingly, vision transformer architectures trained on large annotated image datasets. A notable 2026 development is the shift toward foundation models that displace task-specific training for many commercial applications, alongside growing use of agentic vision systems moving from research into operational deployment. Computer vision supports applications including autonomous vehicle perception, medical image analysis, industrial quality inspection, surveillance and security systems, and augmented reality. As an open-access computer vision journal, IJACSA publishes research on computer vision algorithms, model architectures, and applied vision systems evaluated on standard and domain-specific image datasets.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
Although cloud storage platforms are widely used to safeguard personal images, data leakage and unauthorized access remain persistent threats, exposing sensitive visual content. Linear obfuscation methods such as Gaussia…
Apple diseases cause great losses in fruit yield and quality, while manual diagnosis is time-consuming, subjective, and difficult to scale across orchards. Deep learning has thus become a staple in image-based disease re…
Conventional feature fusion mechanisms largely overlook orientation information, making it difficult to effectively represent objects with diverse rotational patterns. To address this issue, we propose YOLO-RSL, a lightw…
Tumors of the lymphatic system can be either benign or malignant. However, because traditional diagnostic approaches rely on subjective assessment by individual pathologists and often lead to significant discrepancies an…
Interpreting orthopantomograms (OPGs) accurately and efficiently remains challenging due to overlapping anatomical structures and the subtle presentation of many dental pathologies. While deep learning (DL) has shown con…
Self-supervised learning (SSL) has become a powerful paradigm for visual representation learning, yet it remains insufficiently understood how different training strategies shape the balance between global semantic struc…
Estimating crowd size in dense environments re-mains a complex problem, yet it holds critical value for safety monitoring, city infrastructure design, and large-scale gathering coordination. Leveraging contemporary devel…
Robust inspection of aeroengine turbine blades remains a critical challenge in safety-critical industrial environments, where limited data availability, class imbalance, optimisation bias, and imaging degradation can red…
Deep neural network (DNN)-based object detec-tors are widely used for analyzing aerial and satellite imagery in applications such as environmental monitoring and urban analytics. Despite their strong performance, these m…
Fire is a major hazard in many disasters, creating risks to public safety and the surrounding environment. This study aims to improve the accuracy and reliability of fire detection using deep learning, addressing the key…
