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
“Food is the most important thing for the people”, Food is intricately linked to both the national economy and the livelihood of the people, serving as a vital material for our daily existence. Wheat, standing as one of…
For the motion capture driving technology of three-dimensional animation, this study combines skeleton extraction methods and human motion pose data to construct the human skeleton of three-dimensional animated character…
Edge detection is essential for image processing and recognition. However, single methods struggle under challenging lighting conditions, limiting the effectiveness of applications like sign language recognition. This st…
In this paper, we propose the FEC-IGE framework includes data preprocessing, data augmentation, transfer learning, and fine-tuning of the pre-trained model of convolutional neural network (CNN) architecture for the probl…
As technology advances, solving image segmentation challenges in complex backgrounds has become a key issue across various fields. Traditional image segmentation methods underperform in addressing these challenges, and e…
It is important to note that the cure rates in cases of advanced stages of lung cancer are remarkably low, which stresses out the importance for early detection as means to increase survival chances. A strong area of foc…
In the field of visual communication, image clarity and accuracy are the key to convey effective information. A new sparsity-enhanced image processing model is introduced to address the limitations of traditional image p…
This research paper investigates the development of deep learning models for traffic sign recognition in autonomous vehicles. Leveraging convolutional neural networks (CNNs), the study explores various architectural conf…
Population growth and urbanization demand innovative strategies for sustainable city management. This paper focuses on the integration of the Internet of Things (IoT) and image processing technologies for environmental m…
As an important ornamental plant, the automatic detection and classification of the maturity of Alstroemeria Genus Morado flowers hold significant importance in precision agriculture. However, this task faces numerous ch…
