Authors: Assistant Professor Benasir Begam.F, Assistant Professor Agalya.A, Assistant Professor Gopalakrishnan T
Abstract: 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.
