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
In recent years, the Kingdom of Saudi Arabia has witnessed a noticeable growth of grass and small trees in the desert, forming green patches. Those green patches may have the potential to spread and cover a wider area in…
With an increasing demands of video tracking systems with object detection over wide ranges of computer vision applications, it is necessary to understand the strengths and weaknesses of the present situation of approach…
On image sharing websites, the images are associated with the tags. These tags play a very important role in an image retrieval system. So, it is necessary to recommend accurate tags for the images. Also, it is very impo…
In computer aided diagnosis of liver tumor detection, tumor segmentation from the CT image is an important step. The majority of methods are not able to give an integrated structure for finding fast and effective tumor s…
Security of house doors is very important and becomes the basis for the simplest and easiest security and sufficient to provide a sense of security to homeowners and along with technological developments, especially in t…
In this paper, we introduce a novel band selection approach based on the Kolmogorov Variational Distance (KoVD) for Hyperspectral image classification. The main reason we are taking interest in KoVD is its unique relatio…
Gastrointestinal parasitic diseases represent a latent problem in developing countries; it is necessary to create a support tools for the medical diagnosis of these diseases, it is required to automate tasks such as the…
In recent years, various encoder-decoder-based U-Net architecture has shown remarkable performance in medical image segmentation. However, these encoder-decoder U-Net has a drawback in learning multi-scale features in co…
Quantum Computer is a computer machine using principles of quantum mechanics in doing its computation. The Quantum Computer Machine itself is still in the development stage and has not been deployed yet, however TensorFl…
Hyperspectral images are used to recognize and determine the objects on the earth’s surface. This image contains more number of spectral bands and classifying the image becoming a difficult task. Problems of higher numbe…
