What is the difference between computer vision and machine vision?

Artificial intelligence is a general term covering several specific technologies. In this article we will explore machine vision (MV) and computer vision (CV). They all involve visual input, so it is important to understand the advantages, limitations, and best use case scenarios of these overlapping technologies.

Artificial intelligence is a general term covering several specific technologies. In this article we will explore machine vision (MV) and computer vision (CV). They all involve visual input, so it is important to understand the advantages, limitations, and best use case scenarios of these overlapping technologies.

Researchers began to develop computer vision technology as early as the 1950s, starting with simple two-dimensional imaging for statistical pattern recognition. It was not until 1978 when researchers at the MIT Artificial Intelligence Laboratory developed a bottom-up method to infer 3D models from “drafts” created by 2D computers, the practical application of computer vision became obvious. Since then, image recognition technologies have been divided into different categories through general use cases.

Both computer vision and machine vision use image capture and analysis to perform tasks with speed and accuracy that the human eye cannot match. With this in mind, it may be more effective to describe these closely related technologies through their commonalities, and to distinguish them through their specific use cases rather than their differences.

Computer vision and machine vision systems share most of the same components and requirements:

Imaging device containing image sensor and lens

You can use an image capture board or frame grabber (in some digital cameras that use modern interfaces, no frame grabber is needed)

Applicable lighting

Software that processes images through a computer or internal system, such as many “smart” cameras

What is the difference between computer vision and machine vision?

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 only to see, but also to process and provide useful results based on observations. Machine vision refers to the use of computer vision in an industrial environment, 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 field of digital marketing, companies began to use image recognition technology to promote better advertising and business results. As the accuracy and efficiency of computer vision technology continues to improve, marketers can now bypass traditional demographic research and quickly and accurately comb through millions of online images. Then, they can conduct targeted marketing in the right context, and people only need to spend a fraction of the time to get the same results.

Machine Vision and Smart Factory

The ability to visually identify problems such as product defects and process inefficiencies is essential for manufacturers to limit costs and increase customer satisfaction. Since the 1990s, machine vision systems have been installed in thousands of factories around the world to automate many basic quality assurance and efficiency functions. With enhanced data sharing capabilities and higher precision provided by innovative cloud technology, the use of machine vision drive systems in the manufacturing industry has begun to accelerate. Manufacturers realize that machine vision systems are an important investment in achieving quality, cost, and speed goals.

Machine vision on the production line

Detecting defects and quickly mitigating the causes of these defects is an important aspect of any manufacturing process. Longrui Zhike turned to machine vision solutions to proactively solve the occurrence and root causes of defects. By installing cameras on the production line and training machine learning models to identify complex variables that define good products and defective products, defects can be identified in real time and where the defects occur in the manufacturing process. So proactive measures can be taken.

Annotate machine learning models for vision technology

In order to achieve computer or machine vision goals, you first need to train machine learning models that make your vision system “intelligent”. And in order to make the machine learning model accurate, a large amount of annotated data is required, which is specific to the solution reconstruction. There are free publicly-used data sets that can be used to test algorithms or perform simple tasks, but to make most practical projects successful, special data sets are needed to ensure that they contain the correct metadata. For example, implementing a computer vision model in an autonomous vehicle requires a large number of image annotations to mark people, traffic signals, cars, and other objects. Anything below the total accuracy will become a huge problem for autonomous vehicles.

Related technologies with different use cases

Although the boundary between computer vision and machine vision has been blurred, the two 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 in actual interfaces, such as factory production lines.

Custom machine vision service

Modern vision systems are designed to provide improved image quality and are ideal for image restoration, image encoding and image interpretation. Whenever industrial applications require identification, guidance or measurement, machine vision is a widely used option.

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