Artificial Intelligence for Computer Vision: Bibliometric Analysis

Abstract

Computer Vision (CV) is a prominent area of focus in Artificial Intelligence (AI) research, with applications ranging from self-driving cars to medical imaging. A bibliometric analysis is presented in this study for the latest research in AI for CV, focusing on advancements in CV models, their evaluation, and their use in various applications from 1981 to 2023 using Web of Science Clarivate Core Collection database and a dataset of 1857 retrieved publication. VOS viewer and CiteSpace software were implemented to perform science mappings and bibliometric analysis techniques in the study. Hence, analysing citation networks, publication output, and collaboration patterns in the field to identify influential research publications, researchers, and institutions. The analysis reveals the top publications and researchers in the field, as well as the most common research topics and their relative importance. This study finds that deep learning techniques, such as convolutional neural networks (CNNs), are the dominant approach in CV research, with applications in object detection, feature extraction, and image analysis. Also, it found that USA has a wide partnership and collaborative range amongst making it the most productive country. This study also discussed few of the challenges and opportunities in AI for CV, including U-Net not generating more precise segmentation in biomedical image segmentation. The recommendation of this study for future research direction is the need for more interdisciplinary collaboration, the development of new evaluation techniques, and the exploration of novel applications of AI for CV. The bibliometric analysis results will be of interest to researchers, practitioners, and policymakers interested in AI, CV, and related fields, as well as anyone interested in the latest advancements in this exciting and rapidly evolving field.