Advancements and Applications of Machine Vision: A Review of Computational Paradigms and Future Prospects in Intelligent Systems

Machine vision, a sub-discipline of computer science and artificial intelligence, has evolved into a robust
technological framework that enables machines to interpret and make decisions based on visual data. This review delves into
the computational underpinnings of machine vision, tracing its development from classical image processing techniques to
state-of-the-art deep learning architectures. Special emphasis is placed on domain-specific applications such as autonomous
navigation, medical diagnostics, and smart manufacturing, highlighting how vision-enabled machines are reshaping real-world
operations. The paper further explores benchmark datasets, evaluates key performance metrics, and outlines critical
challenges. It concludes with a forecast of emerging paradigms—such as transformer-based vision models and neuromorphic
computing—that promise to redefine the future of intelligent visual systems.