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
Monitoring rice spikelet yield is crucial for ensuring food security, but manual observations are tedious and subjective. Deep learning approaches for automated counting often require high device resources, limiting thei…
Human Action Recognition and Medical Image Segmentation study presents a novel framework that leverages advanced neural network architectures to improve Medical Image Segmentation and Human Action Recognition (HAR). Gate…
In the field of orthodontics, the accurate identification of cephalometric landmarks in dental radiography plays a crucial role in ensuring precise diagnoses and efficient treatment planning. Previous studies have demons…
This paper addresses the challenge of accurately detecting and recognizing Arabic license plates, particularly those subjected to severe tilt angles. It presents a robust license plate detection and recognition framework…
Ischemic stroke is the most prevalent type of stroke and a leading cause of mortality and long-term impairment globally. Timely identification, precise localization, and early detection of ischemic stroke lesions brain a…
Smart health houses have shown great potential for providing advanced healthcare services and support to individuals. Although various computer vision based approaches have been developed, current facial expression analy…
Semantic and instance segmentation are critical goals that span a wide range of applications, from autonomous driving to object recognition in different fields. The existing approaches have limitations, especially when i…
Brain tumors are a prevalent issue in contemporary society as they impact human health. The location of the tumor in the brain determines the variety of symptoms that may manifest. Some frequent symptoms are cephalalgia,…
Due to the high cost and time-consuming nature of acquiring labelled samples of hyperspectral data, classification of hyperspectral images with a small number of training samples has been an urgent problem. In recent yea…
In this innovative exploration, "Applying Computer Vision Techniques in STEM-Education Self-Study," the research delves into the transformative intersection of advanced computer vision (CV) technologies and self-directed…
