Have you ever asked yourself how a computer "sees" a picture in terms of what it can tell us about what's in the picture? And it’s all due to something known as machine learning vision systems. Machine learning is a form of technology that enables computers to learn how to do something — just like we can learn through experience and do something better over time.
A machine learning vision system, in plain English, is a computer program that can “see” and understand the images it’s presented. It does so by both looking for patterns and data in the images, and then making predictions about the images given what it learned from previous examples.
Machine learning vision systems have progressed a long way in recent years. It used to be that computers could only detect simple shapes and colors. But as technology has improved, the ability of machines to comprehend and interpret highly complex images has also gotten better.
A major step in the evolution of machine learning in visual recognition is the application of neural networks. These networks are supposed to function like our own brains, with layers of artificial neurons that process things and make linkages. Machines have made breakthroughs in recognizing objects, faces and even emotions in images by using neural networks.
When you take a photo with a smartphone, have you ever had it automatically identify people in the frame? All of that is thanks to machine learning vision systems. These include algorithms to recognize and categorize objects within images — which helps, say, when you need to sort through old photos.
Think of a world where machines can instantly recognize and follow our objects. This can now be achieved using machine learning vision systems. Such systems are designed based on computer vision approaches to detect and recognize objects within a captured image or video.
It is, for example, widely used in machine learning vision systems for object detection, such as in the automotive field (self-driving cars, etc.). These systems help the cars “see” and respond to their environment, like other vehicles, pedestrians and traffic lights. And by leveraging machine learning, these cars can drive safely and avoid collisions in the street.
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