Definition
Computer Vision (CV) is the field of AI that enables computers to get meaning from images and video. Its main tasks are classifying images, detecting objects, segmenting images pixel by pixel, and tracking things over time.
In simple words
To you a photo shows a dog on a sofa. To a computer it's a grid of numbers. Computer vision is how those numbers become "dog" and "sofa". Face unlock, UPI QR scanners, and lane-assist in cars all use it.
Why it matters
Cameras are cheap and everywhere, and vision turns them into sensors that understand. It's used for medical scan analysis, factory quality checks, crop disease detection and document reading.
How it works
| Task | Question it answers | Output |
|---|---|---|
| Classification | What is in this image? | One label |
| Object detection | What's here, and where? | Boxes with labels |
| Semantic segmentation | Which class is each pixel? | A label per pixel |
| Instance segmentation | Which object is each pixel part of? | A mask per object |
| OCR | What text is in the image? | Text |
| Pose estimation | How is the person positioned? | Body keypoints |
Two generations
Classical vision uses hand-designed steps such as edge detection and SIFT features, and it's still useful where data is small or hardware is limited. Since 2012, deep networks have learned features directly from pixels: first CNNs, and now also Vision Transformers.
Try it yourself
From pixels to “dog”
You see a dog at once. A computer gets there in stages.
See a picture as the computer sees it
Click squares to draw. Every square is a pixel, and the computer stores each one as a number.
Pick the right task
Each person needs something different from a photo. Match each need to the vision task that does it.
- "Is there a dog in this photo?"
- "Draw a box around every car."
- "Mark every pixel that is road."
- "Read the number on this number plate."
- "Where are this dancer's arms and knees?"
Common mistakes
- Trusting high test accuracy when the test photos look just like the training photos. Real lighting and cameras differ.
- Ignoring bias and consent in face recognition. It has real legal and ethical risks.
Check yourself
Summary
- CV gets meaning from images and video.
- Main tasks: classification, detection, segmentation, OCR and pose.
- To a computer, an image is a grid of numbers.
Finished reading?
Sources: Stanford CS231n