“Artificial intelligence is an umbrella term that covers several specific technologies. In this article we will explore machine vision (MV) and computer vision (CV). They all involve visual input, so it’s important to understand the strengths, limitations, and best use-case scenarios of these overlapping techniques.
Artificial intelligence is an umbrella term that covers several specific technologies. In this article we will explore machine vision (MV) and computer vision (CV). They all involve visual input, so it’s important to understand the strengths, limitations, and best use-case scenarios of these overlapping techniques.
Researchers began developing computer vision techniques as early as the 1950s, starting with simple two-dimensional imaging for statistical pattern recognition. It wasn’t until 1978 that the practical application of computer vision became apparent when researchers at MIT’s Artificial Intelligence Laboratory developed a bottom-up approach to infer 3D models from “sketches” created by 2D computers. Since then, image recognition technology has been divided into different categories by general use cases.
Both computer vision and machine vision use image capture and analysis to perform tasks with speed and accuracy unmatched by the human eye. With this in mind, it may be 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 including an image sensor and a lens
Either an image capture board or a frame grabber can be used (in some digital cameras using modern interfaces, a frame grabber is not required)
Lighting for the application
Software that processes images through a computer or internal system, like many “smart” cameras
So what is the actual difference? Computer vision refers to the automation of image capture and processing, with an emphasis on image analysis. In other words, the goal of computer vision is not just to see, but to process and deliver useful results based on observations. Machine vision refers to the use of computer vision in industrial settings, making it a subcategory of computer vision.
computer vision in action
In 2019, computer vision is playing an increasing role in many industries. In the digital marketing space, companies are beginning to use image recognition technology to drive better advertising and business results. Thanks to the increasing accuracy and efficiency of computer vision technology, marketers can now bypass traditional demographic research and comb through millions of online images quickly and accurately. They can then do targeted marketing in the right context, and it takes people a fraction of the time to get the same results.
Machine Vision and Smart Factory
The ability to visually identify issues such as product defects and process inefficiencies is critical for manufacturers to limit costs and improve customer satisfaction. Since the 90s, machine vision systems have been installed in thousands of factories around the world, automating many essential quality assurance and efficiency functions. The use of machine vision-driven systems in manufacturing has begun to accelerate with enhanced data sharing capabilities and higher precision provided by innovative cloud technologies. Manufacturers realize that machine vision systems are an important investment in meeting quality, cost, and speed goals.
Machine Vision on the Production Line
Detecting defects and quickly mitigating the causes of those defects is an important aspect of any manufacturing process. Langrui Zhike turned to machine vision solutions to proactively address the occurrence and root cause of defects. By installing cameras on the production line and training a machine learning model to identify the complex variables that define good versus bad products, defects can be identified in real-time and determine where in the manufacturing process they occur so proactive action can be taken.
Annotating Machine Learning Models for Vision Technologies
To achieve computer or machine vision goals, you first need to train a machine learning model that makes your vision system “smart”. And for machine learning models to be accurate, large amounts of annotated data, solution-specific reconstructions, are required. There are free public-use datasets available to test algorithms or perform simple tasks, but for most real-world projects to succeed, specialized datasets are required to ensure they contain the correct metadata. For example, implementing computer vision models inside autonomous vehicles requires extensive image annotation to label people, traffic signals, cars, and other objects. Anything less than total accuracy is going to be a huge problem for self-driving cars.
Related technologies with different use cases
While the line between computer vision and machine vision has blurred, both are best defined by their use cases. Computer vision is traditionally used to automate image processing, and machine vision is the application of computer vision to real-world interfaces, such as factory production lines.
Custom Machine Vision Services
Modern vision systems are designed to provide improved image quality and are ideal for image restoration, image encoding, and image interpretation. Machine vision is a widely used option whenever an industrial application requires identification, guidance or measurement.