What is computer vision?
Computer vision(CV) is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do. The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it. Technically, machines attempt to retrieve visual information, handle it, and interpret results through special software algorithms.
Here are few common tasks that computer vision systems use:
- Object classification. The system parses visual content and classifies the object on a photo/video to the defined category. For example, the system can find a dog among all objects in the image.
- Object identification. The system parses visual content and identifies a particular object on a photo/video. For example, the system can find a specific dog among the dogs in the image.
- Object tracking. The system processes video finds the object (or objects) that match the search criteria and tracks its movement.
Computer vision resembles a jigsaw puzzle
Computers assemble visual images in the same way you might put together a jigsaw puzzle.
Think about how you approach a jigsaw puzzle. You have all these pieces, and you need to assemble them into an image. That’s how neural networks for computer vision work. They distinguish many different pieces of the image, they identify the edges and then model the subcomponents. Using filtering and a series of actions through deep network layers, they can piece all the parts of the image together, much like you would with a puzzle.
The computer does not give a final image on the top of a puzzle box. But is often fed hundreds or thousands of related images to train it to recognize specific objects.
Instead of training computers to look for whiskers, tails, and pointy ears to recognize a cat, programmers upload millions of photos of cats, and then the model learns on its own the different features that make up a cat.
How does CV work?
CV technology tends to mimic the way the human brain works. But how does our brain solve visual object recognition? One of the popular hypotheses states that our brains rely on patterns to decode individual objects. This concept is used to create computer vision systems.
Computer vision algorithms that we use today are based on pattern recognition. We train computers on a massive amount of visual data—computers process images, label objects on them, and find patterns in those objects. For example, if we send a million images of flowers, the computer will analyze them, identify patterns that are similar to all flowers, and, at the end of this process, will create a model “flower.” As a result, the computer will be able to accurately detect whether a particular image is a flower every time we send them pictures.
Where we can apply computer vision technology
Computer vision is integrated into many areas of our life. Below are just a few notable examples of how we use this technology today.
Content organization
Computer vision systems already help us organize our content. Apple Photos is an excellent example. The app has access to our photo collections. And it automatically adds tags to photos and allows us to browse a more structured collection of photographs. What makes Apple Photos great is that the app creates a curated view of your best moments for you.
Facial recognition
Facial recognition technology is used to match photos of people’s faces to their identities. This technology is integrated into major products that we use every day. For example, Facebook is using computer vision to identify people in photos.
Facial recognition is a crucial technology for biometric authentication. Many mobile devices available on the market today allow users to unlock devices by showing their faces. A front face camera is used for facial recognition. Mobile devices process this image and, based on analysis, can tell whether the person who is holding a device is authorized on this device.
Self-driving cars
Computer vision enables cars to make sense of their surroundings. A smart vehicle has a few cameras that capture videos from different angles and send videos as an input signal to the CV software. The system processes the video in real-time and detects objects like road marking, objects near the car (such as pedestrians or other cars), traffic lights, etc. One of the most notable examples of applications of this technology is autopilot in Tesla cars.
Health Sector
Computer vision has also been an important part of advances in health tech. CV algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scans.
Conclusion
In conclusion, we have studied what is computer vision, how it works, and where we are implementing computer vision in our life. Therefore now we know, why Computer vision is so popular.
Happy Reading !! 🙂
