Machine vision vs computer vision: everything you need to know

Every day, AI (artificial intelligence) helps in industrial process automation, increasing operational efficiency and even improving safety in our daily lives. Machine vision and computer vision are at the forefront of visual AI. But what are these fields responsible for and how are they different?

In this comprehensive guide, we discuss machine vision vs computer vision, their similarities, differences and how they are used in everyday life.

What is machine vision?

Machine vision refers to the technology, methods, software and hardware involved in processing visual input, usually in an industrial environment. Simply put, machine vision is a system that captures images of a given environment using special cameras. This information is then processed and used for various applications, such as visual inspection and object detection (which we discuss in more detail below).

How does machine vision work?

First, high-quality cameras capture images of the environment. From there, certain predefined aspects of these images are processed. For example, a machine vision algorithm could be trained to detect stop signs in photographs. If the system takes an image of a street with several houses on each side, it will only look for the presence of a stop sign.

What is computer vision?

Computer vision is an application of AI for processing and analyzing visual content such as images and video. The main purpose of computer vision is task automation. It enables a computer to "see" and analyze visual content, similar to how a human would do it. Computer vision is often used for object identification in various fields and applications, ranging from manufacturing environments to vehicle applications (which we discuss in more detail below).

How does computer vision work?

Computer vision works by repeatedly processing and analyzing visual input until the algorithm has learned to recognize patterns and objects. Let's take the same example we used for machine vision. A computer vision system could analyze an image of a street with several houses on each side. It could then identify the houses, the street and various signs, analyze this data and conclude that it is an image of a residential street.

Machine vision vs computer vision; how different are they?

With so many similarities and overlap between the two fields, it can be difficult to tell them apart. Let's dig deeper into it and look at the similarities and differences.

Image processing versus analysis:

One of the biggest differences between machine vision and computer vision is the way visual input is used. Machine vision is used to process the most important information in an image based on what the algorithm is trained to look for. Computer vision, on the other hand, requires significantly more processing and analyzes the entire image.

Machine vision is often used for automated tasks. It works software-based with fixed, strict parameters that tell cameras exactly what to look for. For example, a computer can be trained to quickly recognize certain defects in products on a production line so that they can be removed.

However, computer vision does not have the same parameters. If you see a picture of a person you have never seen before, you can recognize him as a person even though you have never seen his face. Similarly, computer vision algorithms are fed large amounts of information to "teach" them how to recognize certain objects. This mimics how humans process images. As a result, computer vision is often used for more complex tasks such as image classification.

Machine vision recognizes parts of images. Computer vision recognizes images and also draws inferences from them.

Visual media: 

Machine vision can only process images created by the system's cameras. However, computer vision can process multiple forms of visual input, including images and video from multiple sources, such as cameras, motion detectors, thermal sensors, radar and LiDAR.

Real-time versus historical images:

Machine vision and computer vision both operate based on visual input captured by video cameras. However, machine vision is limited to processing images captured by the system's cameras. Computer vision can process pre-existing visual media. If the application only needs to analyze stored media, rather than images captured in real time, the computer vision system does not need cameras.

Integrated vs standalone use:

Because they require specific software, machine vision systems cannot work in isolation. They must be integrated into a larger system. Many machine vision systems are also used alongside computer vision. Computer vision systems, on the other hand, can be used as stand-alone solutions.

Examples of machine vision

• Quality assurance: Because machine vision speeds up and automates tasks, it is often used for visual inspection on production lines. With a machine vision system, cameras capture images of products on the production line and detect production deviations, such as food packaging that is not properly sealed or parts of a product that are not properly soldered.
• Sorting objects: tomato grading machines use machine vision to sort tomatoes by color. If the camera detects that the color of the tomato is green or otherwise unsuitable for the purpose for which it will be used, the product can then be quickly and automatically removed.

Examples of computer vision

• Production safety: The use of computer vision makes production environments safer by analyzing live video images. It can detect whether or not personnel are wearing personal protective equipment, such as reflective vests, safety helmets, gloves or safety glasses.
• Vehicles: Many modern cars have object recognition that uses computer vision. Lane detection, for example, has become a more common option. This technology identifies road markings and helps drivers approaching or crossing the lines by gently guiding the car back into the correct lane.

Looking for your AI vision solution, or have questions about machine vision vs computer vision? Check out our line machine vision computers, which are also ideal for computer vision applications, and contact OnLogic. For more information, please visit the customer cases of Artemis Vision and Flasheye read. In it, you can see how OnLogic became the platform for their machine vision and computer vision solutions, respectively.