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 underwater investigations and research remain challenging due to various underwater distortion factors, scattering, and low wavelength absorption. Hyperspectral imaging helps in obtaining detailed information on each…
This study evaluates three classification scenarios: image-based only, acoustic-based using Mel Frequency Cepstral Coefficients (MFCC), and a combined multimodal CNN architecture integrating both modalities. The experime…
Accurate crowd counting in real-world scenes re-mains challenging due to severe occlusions, perspective distortion, and large intra-scene density variation. Recent deep learning based approaches typically address these c…
As intelligent manufacturing advances toward precision and automation, cutting tool condition critically impacts product quality, equipment safety, and production efficiency. Anomalies like wear, chipping, or fracture ca…
The accurate visual analysis of fruit maturity in complex agricultural scenes remains a fundamental challenge due to gradual appearance changes, object overlap, and partial occlusion. This study addresses tomato maturity…
Recent advances in Artificial Intelligence (AI) and Computer Vision have significantly enhanced the potential of Advanced Driver Assistance Systems (ADAS). However, existing solutions remain limited by high computational…
Corn has particular importance in the global food industry. Many diseases attack the corn crops, which affects the crop yield. Early classification and detection of these diseases are pivotal to preventing damage and ach…
This study presents the design, development, and evaluation of an intelligent fruit-picking robot that integrates convolutional vision, adaptive gripping mechanisms, and kinematic control to enable automated harvesting i…
Deep learning (DL) has become a transformative approach in medical image analysis, offering superior accuracy and automation in image segmentation tasks. In reproductive imaging, transvaginal ultrasound (TVUS) serves as…
Cone-beam computed tomography (CBCT) imaging in dentistry requires post-reconstruction image processing to enhance diagnostic quality while minimizing radiation exposure. Visualization of fine dentomaxillofacial structur…
