According to Grand View Research, the global computer vision market size was valued at $12.22 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 7.3% from 2021 to 2028, bringing the value of the computer vision market to an impressive $20.05 billion.
How Computer Vision Works
IBM defines computer vision as “a field of artificial intelligence that enables computers and systems to derive meaningful information from digital videos, and other inputs.” Adobe explains it as, “Computer vision 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.”
Human vision means seeing an object with our eyes, and then interpreting what that object is and what it means using our brain and our past experiences with that object.
Computer vision requires a sensing device equivalent to our eyes, and then an interpreting device like our brains to make sense of the seen object. Computer vision can do different types of tasks, like object classification, object identification, and object tracking.
Computer Vision in Action
Computer vision started slowly in the 1950’s and has made its way into our everyday lives, whether you know it or not. Think of the facial recognition technology on your iPhone. That is an example of computer vision. Meta’s photo tagging feature? That is also computer vision in action.
Tesla uses computer vision for its self-driving cars and your safety. In addition, doctors use computer vision to diagnose diseases from MRIs, X-rays, and mammography more quickly and accurately.
Computer vision has become so accurate that it is even more reliable than humans at processing and responding to visuals, which improves our quality of life and saves more lives.
“Gaining” Computer Vision
While the concept of learning and recognizing images and videos is easy enough for us humans to understand, the process for an AI to know on the same level is complicated and time-consuming. For computers to “gain vision,” they must use pattern recognition, like we humans use to learn.
Computers require obscene data to function like our human eyes and brains. For example, algorithms need millions of pictures and angles of the same type of object to recognize the thing in different forms. This type of deep learning is the most effective way for computers to learn “vision.” Their algorithm uses a neural network to sense patterns from large amounts of data, much like how the human brain learns to process sight.
Why Computer Vision is Important Today
Why is computer vision more important now than ever? With evolving technology, we can input more data than ever before for computers to learn and identify pictures.
In addition, computers are getting smarter every day and at rates that human brains have trouble comprehending. All this to say that we can use this snowball effect to improve our apps, businesses, and our livelihoods.
Become a Computer Vision Expert
Learn how to master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models with the Computer Vision Nanodegree.