Embedded vision systems: The radical future ahead

Machine learning will change embedded vision systems as we know them, leading to products with entirely new, futuristic capabilities.

07/05/2017


The increasingly small size of cameras, as well as increasing processing and algorithmic capabilities, have made embedded vision systems a possibility. Courtesy: Patrick Tomasso

Embedded vision systems are a relatively new addition to the world of machine vision and vision streaming, but they have the potential to radically transform the future of vision systems. Most embedded vision systems deploy a combination of some of the most recent imaging and processing technology to provide entirely new applications. These vision systems are poised to transform the future of image streaming and image capture.

A brief explanation of embedded vision systems

Embedded vision systems are just that - an imaging system built into another mechanical system designed for some other purpose. These image systems integrate into a larger product in an effort to bring it into the real world and bring vision to a separate invention.

It's important to note, however, that it's only recently that the increasingly small size of cameras, as well as increasing processing and algorithmic capabilities, have made embedded vision systems a possibility.

Another technological trend is poised to transform embedded vision systems, helping them achieve their ultimate goal of providing vision to the products they're embedded in.

The radical future of embedded vision systems

Machine learning will change embedded vision systems as we know them, leading to products with entirely new, futuristic capabilities.

Deep learning capabilities can help embedded vision systems actually recognize what they are seeing by teaching them the pattern and parameters of different objects. This concept could even extend to an image's context. Being able to recognize and distinguish a baby from a picture is an impressive ability. Machine learning could go a step farther and say that this baby is sleeping next to a teddy bear.

This is a very simplistic example, and image classification capabilities are quickly surpassing those of humans (3.46% error vs 5.1% error in a recent study, respectively). Machine learning can put embedded vision systems in autonomous vehicles, robotics, medical devices, security systems and much more, giving each scenario the ability to recognize images and make decisions based on those images.

Embedded vision systems are going to be an important part of the technological future, and machine learning capabilities are driving the radical potential of these imaging systems.

This article originally appeared on Vision Online. AIA is a part of the Association for Advancing Automation (A3), a CFE Media content partner. Edited by Hannah Cox, content specialist, CFE Media, hcox@cfemedia.com.

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