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
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…
Road infrastructure monitoring plays a crucial role in ensuring safety and economic efficiency; however, conventional manual inspection methods are expensive and resource-intensive. This study presents the development an…
This study investigates a class-splitting strategy for improving object detection under limited training data using YOLOv11n with transfer learning and data augmentation for agricultural images containing leaves and pepp…
Automated Video Anomaly Detection (VAD) plays a vital role in developing surveillance systems in public spots. Our study develops real-time anomaly detection via a hybrid Convolutional Neural Network–Long Short-Term Memo…
To address the challenges of feature extraction in complex field environments, the limited sensitivity of YOLOv9 to subtle disease features, and the lack of adaptive hyperparameter optimization, this paper proposes an im…
