ADVANCES IN COMPUTER VISION: NEW HORIZONS AND ONGOING CHALLENGES

Abstract

Computer vision, a rapidly evolving field at the intersection of computer science and artificial intelligence, has witnessed unprecedented growth in recent years. This comprehensive review paper provides an overview of the advancements and challenges in computer vision, synthesizing the latest research findings, methodologies, and applications. We explore the historical evolution of computer vision and discuss recent advancements in algorithms and techniques, including deep learning models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). Diverse applications of computer vision across domains such as healthcare, autonomous vehicles, surveillance, and augmented reality are also examined. Despite remarkable progress, computer vision faces significant challenges, including robustness to adversarial attacks, interpretability, ethical considerations, and regulatory compliance. We discuss these challenges in-depth and highlight the importance of interdisciplinary collaboration in addressing them. Additionally, recent trends and future directions in computer vision research, such as self-supervised learning and explainable AI, are identified. By synthesizing insights from academic research and industrial developments, this review paper aims to provide a comprehensive understanding of the current landscape of computer vision and guide future research endeavors.