Computer vision involves the use of artificial intelligence, particularly machine learning and neural networks, to analyze and interpret visual data such as digital images and videos. By leveraging algorithms like deep learning and convolutional neural networks (CNN), computer vision enables machines to derive actionable insights from visual inputs, akin to human vision but at a significantly faster pace using cameras, data, and algorithms instead of biological visual systems.

The historical development of computer vision spans over 60 years, progressing from early experiments on animal vision responses to the development of groundbreaking technologies like Optical Character Recognition (OCR) and image recognition models. Notable advancements include the emergence of neural networks, object recognition focus, and real-time applications like face recognition.

Real-world applications of computer vision cut across various industries from automotive to healthcare, with notable examples such as personalized content curation, language translation, autonomous driving, and quality control in manufacturing. Organizations like IBM provide computer vision solutions and platforms, democratizing access to advanced visual recognition capabilities and making tasks like image classification, object detection, object tracking, and image retrieval feasible, even for users without extensive deep learning expertise. The field continues to evolve, showcasing a promising future with ongoing research driving innovation and expanding its potential impact on daily life and business operations.