Computer vision is a field of computer science and artificial intelligence that aims to enable computers to interpret and understand visual information from the digital world, much like humans do with their sight. Think about how effortlessly you recognize a friend's face, read text on a sign, or navigate around obstacles. Computer vision strives to give machines similar capabilities using digital images and videos as input.

At its core, computer vision seeks to automate tasks that the human visual system performs. Instead of using biological eyes and a brain, computer vision systems use cameras, sensors, algorithms, and computing power. The input is typically a digital image or a sequence of images (video). The output isn't just a processed image; it's some form of understanding or interpretation of what's in the image. This could be:

Identifying objects (e.g., "There is a cat and a dog in this picture").
Recognizing text (e.g., Reading a license plate number).
Estimating the position or movement of objects (e.g., Tracking a ball in a sports game).
Reconstructing a 3D scene from 2D images (e.g., Creating a 3D map for a robot).
Making a decision based on visual input (e.g., Applying brakes in an autonomous car when an obstacle is detected).

Consider a simple example: unlocking your smartphone using facial recognition. The phone's camera captures an image of your face (the input). A computer vision algorithm analyzes this image, extracts distinctive facial features, compares them to stored information, and decides if it's really you (the interpretation and decision).

A simplified view of the computer vision process: visual data enters the system, which then analyzes it to produce meaningful information or actions.

It's important to distinguish computer vision from image processing. While computer vision often uses image processing techniques (like adjusting brightness or applying filters, which you'll learn about later), image processing is more focused on manipulating images or enhancing them for human viewing or for further analysis. Computer vision goes a step further; its ultimate goal is usually not just to transform an image, but to extract meaningful information from it to understand the scene it represents.

Computer vision is an exciting and rapidly evolving field with connections to many other areas, including machine learning (which provides powerful tools for building vision systems), pattern recognition, physics (optics), and signal processing. As you progress through this course, you'll learn the fundamental techniques that allow computers to start making sense of the visual data they receive.