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
The increasing demand for real-time gender and age classification in video inputs has spurred advancements in computer vision techniques. This research work presents a comprehensive pipeline for addressing this challenge…
Active player tracking in sports analytics is crucial for understanding team dynamics, player performance, and game strategies. This paper introduces an innovative approach to tracking active players in handball videos u…
This research investigates the application of open-source transformers, specifically the ConvNeXt V2 and Seg-former models, for brain tumor classification and segmentation in medical imaging. The ConvNeXt V2 model is ada…
With the maturation of remote sensing, the applications of small unmanned aerial vehicles are rapidly expanding. Efficient image object detection algorithms have become crucial for information extraction in unmanned aeri…
Multimodal sentiment analysis extracts sentiments from multiple modalities like text, images, audio, and videos. Most of the current sentiment classifications are based on single modality which is less effective due to s…
This paper aims to present a novel methodology that merges image stitching with super-resolution techniques, enabling the creation of a high-resolution panoramic image from several low-resolution inputs. The proposed app…
“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…
