Computer vision is a field of artificial intelligence that focuses on enabling computers and systems to see, interpret, and understand visual information from the world​. It sits at the intersection of image processing, pattern recognition, and machine learning. In essence, computer vision algorithms take digital images or videos as input and produce some form of output that can be an understanding of the scene – for example, identifying what objects are present, where they are, what action is happening, or even generating a description in words. If “AI enables computers to think”, then one often says “computer vision enables computers to see”​. This involves mimicking aspects of human vision, but also going beyond it in speed and precision for certain tasks.At its core, computer vision involves processing an image at the pixel level and then extracting higher-level features and information​. Key tasks in computer vision include: image classification – determining what main object or category an image belongs to (e.g., does this image contain a cat or a dog?)​, object detection – locating and classifying multiple objects within an image (e.g., finding all instances of cars, pedestrians, etc., in a street photo); segmentation – partitioning the image into regions corresponding to different objects or classes (semantic segmentation labels each pixel by class, instance segmentation further separates individual object instances); pose estimation – finding the pose of persons or objects, such as the positions of human joints in an image; face recognition – identifying or verifying persons in images; and many more specialized problems (like optical character recognition, depth estimation, image captioning, etc.). Modern computer vision heavily relies on deep learning. In particular, convolutional neural networks (CNNs) revolutionized vision in the 2010s by automatically learning hierarchical feature detectors from data (edges -> textures -> parts -> objects), rather than requiring handcrafted filters. For example, a CNN-based model can learn to detect the presence of a cat in an image by training on thousands of cat vs. non-cat images, implicitly learning the visual features that distinguish cats (like fur patterns, face shape, etc.). Vision transformers (ViT and its variants) have also emerged as an alternative architecture, modeling images with self-attention mechanisms. These models now achieve superhuman performance on certain benchmarks of image classification.Computer vision technology has far-reaching applications. In autonomous vehicles, vision systems detect lane markings, traffic signs, and pedestrians to navigate safely. In medical imaging, computer vision algorithms assist in analyzing X-rays, MRIs, or histopathology slides to detect anomalies or disease (e.g., tumor detection). In security and surveillance, vision is used for motion detection, intruder detection, and face recognition.