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
The diagnosis of plant diseases in Capsicum species remains a critical challenge in precision agriculture due to variability in environmental conditions and limited availability of high-quality datasets. The traditional…
Pupillary distance (PD) is an important ocular measurement for optical dispensing and vision-related applications, but standard MediaPipe FaceMesh outputs do not provide true pupil-centre or iris-boundary landmarks when…
The fast development of deepfake technologies has caused growing concerns related to the authentication of digital media, the integrity of personal identification, and the spreading of disinformation. Therefore, there is…
Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, where early and accurate diagnosis plays a vital role in improving survival rates. Recent advancements in deep learning h…
Reconstructing the canonical pose of non-rigid objects from arbitrary depth observations is an important problem in robotic vision, particularly for systems that must perceive, track, and interact with deformable objects…
Modern smart surveillance systems have become a core element of digital forensics workflows, offering real-time detection of weapons, fire, smoke, blood, cars, individuals, and other related objects. These systems improv…
Fine-Grained Image Classification focuses on unique features between visually similar subclasses within a wider category, which remains a challenging task due to low inter-class variations and high intra-class similarity…
Accurate and real-time assessment of road infrastructure is critical for smart city maintenance and transportation safety. However, conventional object detection models often struggle with complex environmental factors,…
With the fast enhancement of deep learning, research on automatic detection of breast tumors is becoming increasingly in-depth. However, traditional CNNs’ linear kernel has difficulty not only in capturing the nonlinear…
With the rapid increase in the number of vehicles on roads, traffic management, and safety enforcement have become significant challenges worldwide. Traditional speed violation detection systems either employ high-end ha…
