Computer vision is a branch of artificial intelligence that enables computers to understand the content of images and videos. It is often paired with natural language processing and various other methods of machine learning, building an understanding of the world from just one input: pictures. This field has many applications, for example, self-driving cars, medical imaging, and robotics. In this article, we will explore some examples of computer vision in action by looking at various ways people are using it today.
What Is Computer Vision?
Before we can understand what makes computer vision so important, we have to talk about what it isn’t. A computer vision system is not actually a computer. There are lots of computers that are capable of running machine learning systems. What makes them different from a general-purpose computer is that they are specialized for performing one particular task—like detecting that a tomato is a tomato or classifying a person’s face. Computer vision systems are made up of three different parts: a camera (of any type), the processor to process the image, and data storage. All of the processing is done in software, which is integrated with the camera hardware (depending on the application).
Medical imaging is one area of computer vision where many great things are happening right now. While this area of computer vision is certainly not new, it is being widely adopted. This is because of the tremendous benefits that it provides to both doctors and patients. Surgical visualization. The use of computer vision to visualize the anatomy of the body is a significant advancement in surgical precision. Using computer vision and machine learning, doctors and surgeons can better visualize and diagnose problems with the human body, and therefore get better results from surgery. And, doctors can also share their findings with other surgeons. Dynamic mapping. Surgical floor plans typically have markers on them that show where items are located on the floor.
Self-driving cars use computer vision technology to perceive what’s around them in the environment around them. In order to do this, they use a variety of sensors that detect the environment around them, including visible and infrared cameras, sonar, GPS, and more. These sensors send data to a computer, which then translates this information into information that the computer needs to get from one set point to another, which in the case of self-driving cars, means avoiding traffic, obstacles, and human drivers. They use maps to remember where they’ve been before, to figure out which side of the road to drive on, which to avoid, and how to act when they see another car or a pedestrian.
With AI, you build an architecture that has the ability to learn, adapt, and optimize over time. As such, any machine learning project is just that: a machine learning project. You start with a very small model, and slowly but surely, as you build more and more models, the architecture learns, adapts, and evolves to meet the needs of the world it is living in. As your architecture learns from more and more input, it ends up being able to solve problems that were simply impossible for the very first iteration. That is the beauty of machine learning: it allows us to build things that simply were not possible before.