Industrial Control Summary : Machine vision is the use of machines to replace human eyes for measurement and judgment. A machine vision system refers to a system that uses machine vision products (i.e., image acquisition devices, which are divided into CMOS and CCD types) to convert the captured target into image signals, which are then transmitted to a dedicated image processing system. Based on pixel distribution and information such as brightness and color, the signals are converted into digital signals. The image system performs various calculations on these signals to extract the features of the target, and then controls the operation of equipment on site based on the judgment results.
When first encountering the terms "machine vision" and "computer vision," they seem similar—both are about vision, and computers are machines, so isn't studying computer vision the same as studying machine vision? Many people might have the same thought. However, delving into these fields reveals that while they share many similarities, they are actually different disciplines. So, what are machine vision and computer vision, what are their differences, and what do they have in common?
Our research into computer vision aims to "bring light" to computers based on the characteristics of human vision, enabling them to better replace humans in certain tasks or perform duties that humans cannot. This would reduce labor costs for businesses, increase production efficiency, and ultimately improve people's quality of life. Researching machine vision also provides greater support for improving product quality and production efficiency in the manufacturing industry.
What is machine vision?
Machine vision, also known as machine vision, uses machines to perform measurement and judgment, replacing the human eye. A machine vision system uses machine vision products (image acquisition devices, either CMOS or CCD) to capture images, which are then transmitted to a processing unit. Through digital processing, the system determines dimensions, shapes, colors, and other characteristics based on pixel distribution, brightness, color, and other information. The system then controls the operation of equipment on-site based on the determination results. Currently, it is widely used in industries such as food and beverage, cosmetics, building materials and chemicals, metal processing, electronics manufacturing, packaging, and automobile manufacturing.
Machine vision is a relatively new technology that offers numerous advantages to the manufacturing industry in improving product quality, production efficiency, and operational safety. Among other related technologies, machine vision encompasses image digitization, image manipulation, and image analysis, typically performed using computers, making it a discipline that covers both image processing and computer vision. However, it's important to emphasize that machine vision, computer vision, and image processing are not synonymous. None of them are subsets of the other two. Computer vision is a branch of computer science, while machine vision is a specific area of system engineering. While machine vision doesn't explicitly require the use of computers, it often utilizes specialized image processing hardware to achieve processing speeds unattainable by ordinary computers.
What is computer vision?
Computer vision refers to using computers to realize human visual functions, enabling the perception, recognition, and understanding of three-dimensional scenes in the objective world. Computer vision is a cutting-edge field. We believe that computer vision, or simply "vision," is a different endeavor than the study of human or animal vision. It utilizes geometric, physical, and learning techniques to construct models, thereby processing data using statistical methods. Therefore, from our perspective, based on a thorough understanding of camera performance and the physical imaging process, vision performs simple reasoning on each pixel, synthesizing information from multiple images into a harmonious whole, determining relationships between pixel sets to separate them, or inferring shape information, using geometric information or probabilistic statistical techniques to identify objects.
With the advancement of technology, scientific research has become increasingly widespread. In various fields, progress requires experimental research, and scientific experiments necessitate specific equipment, with high-speed cameras being indispensable. Vision Image's high-speed, high-resolution digital industrial camera is a high-performance industrial digital camera specifically designed for industrial inspection. It features high resolution, high precision, high definition, excellent color reproduction, and low noise. Utilizing a USB 2.0 standard interface, it is easy to install and use, making it ideal for various indoor and outdoor industrial inspection applications, as well as scientific research. Its features are as follows:
Computer vision is a subfield of artificial intelligence. Its purpose is to enable computers to understand a scene or feature in an image. It includes the following aspects of interpretation:
1. Acquire the necessary data for controlling the target through the automatic acquisition and analysis of images.
2. Provide a navigation system to a robot capable of performing tasks and reacting, enabling it to change position.
3. A system that uses video cameras, robots, or other equipment, and uses computers to perform visual analysis of operations or behaviors. Typical applications include automated monitoring, optical character recognition, and other contactless applications.
4. Machine vision is an application of computer vision in factory automation. Just as a supervisor works on an assembly line, visually monitoring objects and judging their quality, a machine vision system uses cameras and image processing software to accomplish a similar monitoring task. A machine vision system is a computer that makes decisions based on digital image analysis.
In conclusion, there is no clear boundary between machine vision and computer vision; rather, they are closely linked. They share the same theories, differing only in their practical applications. Both computer vision and machine vision aim to extract descriptions of the world from images or image sequences. Therefore, mastering the theoretical knowledge of basic layer image acquisition and processing, intermediate layer image segmentation and analysis, and high-level image understanding is fundamentally the same for both.
