What Is Computer Vision and Machine Vision?

Every day, we open our eyes to see this colorful world, the vibrant flowers, the blue sky, and the familiar smiles of our loved ones. For every healthy person, this is a beautiful privilege granted at birth; we can perceive this world through our eyes. However, do you know how precious vision is for robots? What is the difference between computer vision and machine vision? Today, I will explain what computer vision is and what machine vision is.

First, in many literatures, computer vision and machine vision are used interchangeably, but actually, these two terms have both distinctions and connections.Computer vision employs a combination of image processing, pattern recognition, and artificial intelligence techniques, focusing on the computer analysis of one or more images. Machine vision, on the other hand, emphasizes the engineering application of computer vision technology, capable of automatically acquiring and analyzing specific images to control corresponding actions.

Computer vision refers to the simulation of biological vision using cameras, computers, and other related devices.Its main task is to process the captured images or videos to obtain the three-dimensional information of the corresponding scene, just as humans and many other biological species do every day.

The ultimate goal of computer vision is to enable computers to observe and understand the world through vision, possessing the ability to autonomously adapt to their environment.However, achieving the ability for computers to perceive this world through cameras is extremely challenging. Although the images captured by cameras appear as we see them, for computers, any image is merely an arrangement of pixel values, a collection of rigid numbers. The challenge is to enable computers to extract meaningful visual cues from these rigid numbers, which is the problem that computer vision aims to solve.

However, despite years of development, computer vision still faces a series of difficult problems. The methods currently mastered for specific computer vision tasks apply only to narrow tasks such as face recognition and fingerprint recognition, and cannot be widely applied in different scenarios. Nevertheless, some scholars believe that with the increasing prevalence of machine learning methods and the application of big data technology, a qualitative breakthrough in computer vision is just around the corner.

Machine vision is a rapidly developing branch of artificial intelligence.Simply put, machine vision uses machines to replace human eyes for measurement and judgment. A machine vision system converts the captured target into image signals through machine vision products, transmitting them to a dedicated image processing system to obtain the shape information of the target based on pixel distribution, brightness, color, etc., transforming it into digital signals; the image system performs various calculations on these signals to extract the features of the target, and then controls the actions of the on-site equipment based on the judgment results.

Machine vision is a comprehensive technology that includes image processing, mechanical engineering technology, control, optical source lighting, optical imaging, sensors, analog and digital video technology, and computer hardware and software technology (image enhancement and analysis algorithms, image cards, I/O cards, etc.).A typical machine vision application system includes image capture, lighting systems, image digitization modules, digital image processing modules, intelligent judgment decision modules, and mechanical control execution modules.

The characteristics of machine vision systems are to improve the flexibility and automation of production.In some dangerous work environments unsuitable for manual operations or situations where manual vision cannot meet requirements, machine vision is often used to replace human vision; at the same time, in large-scale industrial production processes, using manual vision to check product quality is inefficient and lacks precision, while machine vision detection methods can greatly improve production efficiency and automation levels. Moreover, machine vision is easy to integrate information, serving as the foundational technology for computer-integrated manufacturing. The image above is a typical application of machine vision.

Undoubtedly, there is considerable overlap between computer vision and machine vision in terms of technology and application fields, indicating that the foundational theories of these two disciplines are roughly the same. However, upon closer examination of their mechanisms, there are indeed some differences:

The research object of computer vision mainly focuses on mapping three-dimensional scenes onto single or multiple images. Much of the research in computer vision is content-oriented.As shown in the image below, how to enable a computer to determine that the images contain cats is the focus of computer vision research.

Machine vision mainly refers to visual research in the industrial field, such as the vision of autonomous robots used for detection and measurement.This indicates that in this field, through software and hardware, image perception and control theory are often closely integrated with image processing to achieve efficient robot control or various real-time operations. To give an inappropriate example, in the image above, machine vision observes hundreds or thousands of cats of a specific shape, identifying which cat is missing an ear and removing it.