Application of AI Computer Vision and Image Recognition

Abstract

In recent years, the intersection of artificial intelligence (AI) and computer vision has significantly reshaped the landscape of image recognition applications across various industries. This review paper provides a comprehensive analysis of the state-of-the-art techniques, methodologies, and advancements in AI-driven approaches for computer vision and image recognition tasks. By examining a multitude of seminal research studies and key developments, this review elucidates the pivotal role of AI algorithms, including deep learning models, convolutional neural networks (CNNs), and generative adversarial networks (GANs), in enhancing the accuracy, efficiency, and robustness of image analysis systems. Additionally, this paper highlights the challenges and limitations faced by AI-driven image recognition, such as data bias, interpretability issues, and ethical considerations, emphasizing the need for further research and development to ensure the responsible and equitable deployment of AI technologies. Ultimately, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the current trends and future directions in leveraging AI for computer vision and image recognition, thereby fostering advancements in diverse domains, including healthcare, autonomous systems, security, and multimedia.