Artificial Intelligence is an umbrella term that covers several specific technologies. In this post, we will explore machine vision (MV) and computer vision (CV). They both involve the ingestion and interpretation of visual inputs, so it’s important to understand the strengths, limitations, and best use case scenarios of these overlapping technologies.
Researchers began developing computer-enabled vision technologies as early as the 1950s, beginning with simple two-dimensional imaging for statistical pattern recognition. It wasn’t until 1978, when researchers at the MIT AI Lab developed a bottom-up approach to extrapolating 3D models from 2D computer-created “sketches” that CV’s practical applications became obvious. Image recognition technologies have splintered into different categories by general use case since then.
Both computer vision and machine vision use image capture and analysis to perform tasks with speed and accuracy human eyes can’t match. With this in mind, it’s probably more productive to describe these closely related technologies by their commonalities—distinguishing them by their specific use cases rather than their differences.
Computer vision and machine vision systems share most of the same components and requirements:
- An imaging device containing an image sensor and a lens
- An image capture board or frame grabber may be used (in some digital cameras that use a modern interface, a frame grabber is not required)
- Application-appropriate lighting
- Software that processes the images via a computer or an internal system, as in many “smart” cameras
So what’s the actual difference? Computer vision refers to automation of the capture and processing of images, with an emphasis on image analysis. In other words, CV’s goal is not only to see, but also to process and provide useful results based on the observation. Machine vision refers to the use of computer vision in industrial environments, making it a subcategory of computer vision.
Computer vision in action
In 2019, computer vision is playing a growing role in many industries. In digital marketing, companies are beginning to use image recognition technologies to drive better ad placement and business outcomes. Thanks to the growing accuracy and efficiency of CV technologies, marketers can now bypass traditional demographic research (which can be problematic in light of data privacy concerns) and quickly and accurately comb through millions of online images. They can then place targeted marketing in the right context, in a fraction of the time it would take for a human to achieve the same result.