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
A self-organizing map (SOM) is a classical neural network method for dimensionality reduction. It comes under the unsupervised class. SOM is a neural network that is trained using unsupervised learning to produce a low-d…
To compete with the international market place, it is crucial for hotel industry to be able to continually improve its services for tourism. In order to construct an electronic marketplace (e-market), it is an inherent r…
Image processing is a discipline which is of great importance in various real applications; it encompasses many methods and many treatments. Yet, this variety of methods and treatments, though desired, stands for a serio…
In this work; we address a novel interactive framework for object retrieval using unsupervised similar region merging and flood fill method which models the spatial and appearance relations among image pixels. Efficient…
Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. This paper…
This paper proposes a new multilevel thresholding method segmenting images based on particle swarm optimization (PSO). In the proposed method, the thresholding problem is treated as an optimization problem, and solved by…
This paper introduces a Bayesian image segmentation algorithm based on finite mixtures. An EM algorithm is developed to estimate parameters of the Gaussian mixtures. The finite mixture is a flexible and powerful probabil…
In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorit…
Chinese Handwritten character recognition is an emerging field in Computer Vision and Pattern Recognition. Documents acquired through Scanner, Mobile or Camera devices are often prone to Skew and Correction of skew for s…
Since last 10 years, various methods have been used for ear recognition. This paper describes the automatic localization of an ear and it’s segmentation from the side poses of face images. In this paper, authors have pro…
