Computer Vision
Computer vision is the field of artificial intelligence that enables machines to interpret and understand visual information from the world. It encompasses the theory, algorithms, and systems for extracting meaningful information from digital images, videos, and other visual inputs, and for using that information to make decisions or generate representations of the visual environment. The field sits at the intersection of computer science, signal processing, physics, and cognitive science, drawing on insights from how biological visual systems process information.
The origins of computer vision date to the 1960s, when Larry Roberts' PhD thesis at MIT demonstrated the first algorithmic interpretation of three-dimensional solids from two-dimensional images, establishing that machines could parse visual information through computation.[^c1] Early research focused on simplified "blocks world" environments, edge detection, and geometric reasoning. The 2012 breakthrough of AlexNet—a deep convolutional neural network that won the ImageNet competition by a dramatic margin—marked the beginning of the deep learning revolution in computer vision, transforming the field from one dominated by handcrafted features to one driven by learned representations.[^c2] In the years that followed, deep learning systems achieved and then exceeded human-level performance on benchmarks for image classification, object detection, and other core vision tasks.[^c3]
Modern computer vision encompasses a broad range of tasks and techniques. Fundamental problems include image classification (assigning labels to entire images), object detection (localizing and classifying objects within images), and semantic segmentation (labeling every pixel with a class). These core capabilities serve as building blocks for applications including autonomous driving, medical image analysis, facial recognition, augmented reality, and industrial inspection. The field has also expanded into generative modeling, with systems that can create photorealistic images and video from text descriptions, and into vision-language models that connect visual understanding with natural language reasoning. As computer vision systems become more capable and are deployed at scale, the field increasingly confronts challenges of fairness, bias, privacy, and the societal implications of automated visual surveillance.