Computer Vision in Artificial Intelligence

Computer Vision (CV) is a subdomain of artificial intelligence that enables computer systems to capture and interpret meaningful information from images and video data, training machines to understand the visual world much the same way as humans do. By applying machine learning models to visual data from the real world, machines can be taught to accurately identify and classify objects and make a decision or take some action based on what they “see”—like unlocking your smartphone when it recognizes your face or automatically steering your car out of harm’s way to avoid an accident.

Computer vision is almost as old as artificial intelligence itself, deriving from early interdisciplinary research spanning psychology, neurobiology, cybernetics, mathematics, and computer science over 60 years ago. Nearly from the beginning, computer vision systems have adopted a hierarchical approach to understanding visual information, similar to the optical systems employed throughout the biological world, building higher-level tasks like object recognition and semantic segmentation composed of lower-level ones like detecting edges and textures.

As noted above, deep learning is a form of representational learning that is particularly well-suited for image data sources—able to automatically extract hierarchical features with increasing complexity across its neural network layers. Like NLP, modern CV has benefited greatly from advances in computing, big data, and the rise of deep learning to achieve performance levels that often exceed humans across a variety of machine vision tasks. Computer vision has become prevalent in modern-day life, with use cases such as optical character recognition, machine inspection, medical image analysis, robotic vision, video surveillance, facial recognition, and human emotion analysis employed extensively in recent years.

Computer vision has enormous utility for businesses across industries, enabling them to generate insights and understanding from images and videos created by the billions of digital camera systems actively deployed worldwide today. Today’s top computer vision use cases are focused on driving operational excellence, cost reduction, and productivity improvements.

In the future, computer vision will merge with other AI systems to increasingly drive greater automation and process improvements, manage risk, ensure regulatory compliance and safety, and unlock visual analytics to make better decisions.