Computer vision

Computer vision is a sub-field of computer science that studies ways to use a computer to process visual information contained in an image or video. The goal is for computers to understand and interpret what they see. Unlike computer graphics (which uses computers to make new images), computer vision takes visual information from the real world and then makes predictions about the contents those images or videos.

 How it works 
While it is easy for humans to look at an image and understand it without any effort, this is very difficult for computers. A computer image is actually just a grid of pixels, so a computer can only interpret an image as a long list of colors like "red red blue black white red...". There is no simple way for a computer to read this list of colors and determine that it contains a picture of a flower, or a dog, or a school bus. Computer Vision is the series of techniques to make sense out of this long list of colors.

At first, the field of computer vision focused on making computers understand images so that they could describe what they see. For example, scientists wanted a computer to be able to say "rabbit" when it was given a picture of a rabbit. It was challenging for scientists to devise ways to help computers understand images, and it was especially hard to get computers to do give the correct answers reliably. Scientists discovered that even when a computer could accurately identify a "normal" rabbit in an image, the computer failed to understand that an upside-down rabbit was still a rabbit.

Early methods that scientists developed are called conventional techniques. They are still useful today. Some examples are:

 Edge detection: picking out the edges between light and dark areas
 Corner detection: picking out corners of sharp shapes
 Blob detection: picking out curvy shapes
 Filtering: ways to adjust an image that makes it easier to pick out shapes, such as blurring it or changing the brightness

Today, computer vision has improved dramatically thanks to advancements in machine learning. Computers can now easily identify objects in images and video, and they do it reliably without making many mistakes. Computers can even understand the context of an image, such as "two rabbits sitting under a bench in a forest at night". This is a much harder problem to solve than just being able to recognize a rabbit, and machine learning enables computers to solve this problem.
alt=Objects detected in an image using a neural network called YOLO.|thumb|301x301px|Objects detected in an image using a neural network called YOLO.
The key tool that enabled this progress is the neural network, which is a way for humans to train a computer to do computer vision tasks. Recent discoveries in better types of neural networks and deep learning has improved computer vision very fast since 2012.

These newer methods are called modern techniques. There are many different techniques in this category, but the most important examples are:

 Convolutional neural networks: a kind of neural network specialized for finding objects in images
 Transformer: a very large and smart type of neural network that is extremely good at Computer Vision tasks
 Recurrent neural network: a type of neural network that can work with sequential data, like video, but not images

Applications
Computer Vision is very helpful to humans because computers can process a lot of information very quickly. Because they are so fast, computers can identify problems faster than humans can, or make measurements more accurately than humans. With Computer Vision, computers can also perform boring tasks that humans don't want to do, like monitoring surveillance video for hours at a time.

Optical Character Recognition

Google Books used scanned images of pages of books to convert those pages to a text format. This allowed users to search the text in the book without needing to read it page by page.

Medical Imaging
Computer systems use the X-Ray and MRI imaging of hospital patients to make a diagnosis as to whether or not they have cancer. In some instances, the computers have outperformed the doctors who make diagnoses on the same patient.

Self Driving Cars
Autonomous Cars use a combination of Computer Vision and LiDAR to detect pedestrians and Stop Signs when driving passengers.

 Infrastructure Inspection 
Checking big structures like bridges, highways, and tunnels for damage is very difficult and dangerous for humans. Today, drones and vehicles with cameras can take thousands of photos of these structures. Computer vision then automatically looks for tiny cracks, rust, or potholes that people might miss. This helps fix problems early before they become dangerous.

 Workplace Safety 
In factories and on construction sites, computer vision is used to make sure everyone stays safe. The system can watch camera feeds to check if workers are wearing their safety gear, like hard hats (helmets) and high-visibility vests. If someone enters a dangerous area without the right gear, the computer can send an alert immediately to prevent injuries.

References

Category:Computer science