Computer vision is a field of artificial intelligence concerned with enabling machines to extract meaningful information from images, videos, and other visual inputs. Systems in this field perform tasks such as object detection, image segmentation, scene classification, and depth estimation by learning hierarchical representations of visual data through neural networks trained on large labeled datasets. The field was substantially reshaped by deep learning after AlexNet demonstrated in 2012 that convolutional neural networks could dramatically outperform traditional feature-engineering methods on benchmark image classification tasks.

Think of it like giving a machine a pair of eyes connected to a trained brain. Just as a person learns to recognize a dog by seeing thousands of examples over a lifetime, a computer vision model builds an internal representation by processing millions of labeled images during training.

A practical example is a quality-control system on a factory line that flags defective components. The model receives a live video feed, runs each frame through a convolutional neural network, and outputs bounding boxes around any part that deviates from the expected shape or surface texture. A simplified inference call might look like:

results = model.detect(frame)
for box, label, conf in results:
    if label == "defect" and conf > 0.85:
        trigger_alert(box)

Computer vision is closely related to Multimodal models, which combine visual and textual understanding, Diffusion models used for image generation, and the Convolutional Neural Network architectures that underpin most vision systems. The Embedding representations learned during training also connect this field to broader topics like Contrastive Learning and Latent Space.

You will encounter computer vision in autonomous vehicles, medical imaging systems, content moderation pipelines, augmented reality applications, and any product that needs to understand or generate pixel-level visual content.