7 Amazing Examples Of Computer And Machine Vision In Practice
Even though early experiments incomputer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text by the 1970s, today the applications for computer vision have grown exponentially. By 2022, the computer vision and hardware market is expected to reach $48.6 billion. It is such a part of everyday life you likely experience computer vision regularly even if you don’t always recognize when and where the technology is deployed. Here is what computer vision is, how it works and seven amazing examples in practice today.
Computer vision is a form of artificial intelligence where computers can “see” the world, analyze visual data and then make decisions from it or gain understanding about the environment and situation. One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better. Our world has countless images and videos from the built-in cameras of our mobile devices alone. But while images can include photos and videos, it can also mean data from thermal or infrared sensors and other sources. Along with a tremendous amount of visual data (more than 3 billion images are shared online every day), the computing power required to analyze the data is now accessible and more affordable. As the field of computer vision has grown with new hardware and algorithms so has the accuracy rates for object identification. In less than a decade,today’s systems have reached 99 percent accuracy from 50 percent making them more accurate than humans at quickly reacting to visual inputs.
One of the critical components to realizing all the capabilities of artificial intelligence is to give machines the power of vision. To emulate human sight, machines need to acquire, process and analyze and understand images. The tremendous growth in achieving this milestone was made thanks to the iterative learning process made possible with neural networks. It starts with a curated dataset with information that helps the machine learn a specific topic. If the goal is to identifyvideos of cats as it was for Google in 2012, the dataset used by the neural networks needs to have images and videos with cats as well as examples without cats. Each image needs to be tagged with metadata that indicates the correct answer. When a neural network runs through data and signals it’s found an image with a cat; it’s the feedback that is received regarding if it was correct or not that helps it improve. Neural networks are using pattern recognition to distinguish many different pieces of an image. Instead of a programmer defining the attributes that make a cat such as having a tail and whiskers, the machines learn from the millions of images uploaded.
Imagine all the things human sight allows and you can start to realize the nearly endless applications for computer vision. Here are some of the most exciting examples of computer vision in practice today:
Computer vision is necessary to enable self-driving cars. Manufacturers such as Tesla, BMW, Volvo, and Audi use multiple cameras, lidar, radar, and ultrasonic sensors to acquire images from the environment so that their self-driving cars can detect objects, lane markings, signs and traffic signals to safely drive.
