How AI Transformed Computer Vision From Fixed Rules to Learning Systems

Computer vision has evolved from brittle, hand-coded rule systems into AI-driven models that learn to interpret images from examples. Early approaches using edge filters and template matching failed whenever lighting, orientation, or surface conditions changed, requiring constant manual re-tuning. Deep learning replaced these rigid rules by training on data, allowing models to generalise across conditions no engineer could anticipate. Convolutional neural networks now handle high-speed industrial inspection tasks, while vision transformers address broader, relational defects across wider scenes. Together, these models are deployed across factory floors, medical imaging systems, and autonomous vehicles, often running on compact edge hardware close to the camera.