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 October 2026
Facial identification has emerged as a key research area due to its potential to enhance biometric security. This research proposes an advanced security system for electric vehicles (EVs) based on facial identification,…
Oil palm trees are the world's most efficient and economically productive oil bearing crop. It can be processed into components needed in various products, such as beauty products and biofuel. In Malaysia, the oil palm i…
Automated detection of intestinal parasites in medical imaging enhances diagnostic efficiency and reduces human error. This study evaluates object detection techniques using Faster R-CNN with different backbone architect…
Illegal mining activities present significant environ-mental, economic, and safety challenges, particularly in remote and under-monitored regions. Traditional surveillance methods are often inefficient, labor-intensive,…
Data-driven design models support various types of mobile application design, such as design search, promoting a better understanding of best practices and trends. Designing the well User Interface (UI) makes the applica…
As Maritime Autonomous Surface Ships (MASSs) increasingly become part of global maritime operations, the reliability and security of their object detection systems have become a major concern. These systems, which play a…
Instance segmentation is a critical component of medical image analysis, enabling tasks such as tissue and organ delineation, and disease detection. This paper provides a detailed comparative analysis of two fine-tuned o…
Visual changes, including spots, discoloration, and deformation characterize coffee leaf diseases. In real-world image data, complex backgrounds present challenges for classification using deep learning models. Irrelevan…
The identification of traffic objects is a basic aspect of autonomous vehicle systems. It allows vehicles to detect different traffic entities such as cars, pedestrians, cyclists, and trucks in real-time. The accuracy an…
Students’ classroom behavior can effectively reflect the learning efficiency and the teaching quality of teachers, but the accuracy of current students’ classroom behavior identification methods is not high. Aiming at th…
