What Is the Difference Between Machine Vision and Computer Vision?
Machine Vision VS Computer Vision
Both machine vision and computer vision can perform tasks with a faster speed than ordinary human vision, but there are some key differences between the two. One difference between computer vision and machine vision is that machine vision systems give vision capability to existing technologies. Machine vision systems involve image processing and work on a set of rules and parameters to support manufacturing applications such as quality assurance. On the other hand, computer vision refers to the capture and automation of image analysis. The image analysis is done by the machine vision system, which can be applied across a wide range of theoretical and practical applications.
What are the Main Differences between Machine Vision and Computer Vision?
While both machine vision and computer vision involve ingesting and analyzing visual inputs, there are differences between the two. Machine vision systems capture images using digital cameras and then process them to output a decision. These decisions can include pass or fail decisions in a production line that are based on a defect that the vision system detected. Machine vision systems also typically contain a camera, lens, processor, and software to enable the machine to make these decisions. In other words, machine vision needs to be part of a larger machine system. On the other hand, computer vision systems can be used on their own.
In addition, computer vision does not necessarily need to capture an image as it can work off of saved images. Computer vision systems can interpret data from a saved image and produce a result or set of results. Unlike machine vision systems, computer vision systems do not require a camera. Computer vision has more flexibility in this regard as it can work by using real-life images or synthetic images. Computer vision systems can gain valuable information from images, videos, and other visuals, whereas machine vision systems rely on the image captured by the system's camera.
Another difference is that computer vision systems are commonly used to extract and use as much data as possible about an object. In contrast, machine vision systems typically focus on specific parts or critical parts of an object and then process that data from its image capture. Since it is used more to find specific qualities, machine vision is normally used for fast decisions in a controlled environment.
Regardless of the differences between the two, the applications of both computer vision and machine vision technologies are immensely diverse.
For a more detailed introduction to machine vision, you can visit our dedicated what is machine vision FAQ.
How do Machine Vision and Computer Vision Work Together?
Computer vision sits within a machine vision system. Machine vision uses computer vision, but the machine vision system also involves the post-image capture part of the process.
The goal of machine vision is to use image capture and process the images to define an action. Machine vision is commonly used in industrial applications such as automatic inspection and manufacturing processes. For instance, manufacturers can use machine vision to process and identify issues like if a product has a defect.
Is Machine Vision a Subset of Computer Vision?
You can look at machine vision as a subset of computer vision. Computer vision is a broader term involving the automation of image analysis without relying on human eyes to do so. It is a field of artificial intelligence that trains computers to interpret and understand images. Machine vision uses computer vision in industrial and practical applications by using image capture to determine an action after interpreting and processing the images. Machine vision can use a frame grabber to capture individual still frames that are commonly high-resolution digital still images and then analyze them. Artificial intelligence can also be used in machine vision to help speed up the system's decision-making process.
What are the Common Applications of Computer Vision?
Computer vision has many practical and important applications. Some of these applications include detecting objects, scanning images, scanning texts, scanning videos, recognizing images, detecting faces, and tracking objects. Computer vision is also used in virtual reality, augmented reality, and more.
In addition, computer vision seeks to automate tasks that the human visual system can do and focuses on how computers can be purpose-built to gain a high-level understanding of digital images or videos. It also includes new applications which leverage advances in machine learning.
What are the Common Applications of Machine Vision?
Machine vision has many practical and important applications such as inspecting objects, detecting flaws in objects, and inspecting packages. Machine vision systems can also be programmed for classifying objects, detecting colors, verifying colors, detecting patterns, and matching patterns. In addition, machine vision is also used when reading barcodes in a structured environment. Because of its wide applications, machine vision is widely used in process control and robot guidance across many businesses and industries.
What are the Basic Functions and Use Cases of Machine Vision?
The combined technologies have applications and benefits within quality assurance and efficiency of operations. More companies are relying on machine vision systems to meet goals surrounding quality and to drive higher customer satisfaction.
Some typical examples of how businesses use machine vision systems include:
1. In the Automotive Industry
Within the automotive industry, machine vision can be used to inspect and determine if a bead has been properly applied, ensuring the assembled parts do not leak and are fully sealed.
These ensure quality and eliminate rework, repair, and scrap, especially as more parts in manufacturing are now being bonded together with adhesives. Both 2D and 3D machine vision can be used to solve these applications. More advanced setups use special machine vision tools to solve bead applications.
An additional use case in automotive is end-of-line inspection and barcode reading. Powertrain/propulsion process inspection to ensure that the engine/transmission is correctly assembled, there are no missing parts, extra “bonus” parts, and that the clips are all in place. Engines/transmissions have many direct parts marked data matrix codes on them, these codes are read at numerous points in the process as part of the track and trace procedures. The same camera can carry out both inspections and code reading.
2. In the Food and Beverage Industry
In the food and beverage industry, machine vision can be used within the bottle cap and fill process. The machine vision system can inspect to determine if the bottle is filled and if the cap is correctly applied to the bottle. This reduces scrap and waste while ensuring that the product is complete and safe.
3. In the Solar Industry
Within the solar industry, the machine vision system can inspect the solar panel assembly process to determine that the panels are correctly built. These can be done by detecting the presence or absence of parts, the location, and the measurement, which ensures that the parts being produced will work when completed and achieve maximum efficiency.
4. Machine Vision and Durable Consumer Goods
In durable consumer goods (e.g., dishwashers, ovens, refrigerators, microwaves) component inspection can determine that the machines and assemblies are correctly built via presence/absence, location, measurement, and color identification tools.
5. Machine Vision and Fastener Manufacturing
In fastener manufacturing, the system can inspect fasteners to ensure that they are properly formed. Normally the tops and the threads of the fastener are inspected. This ensures that the parts are correctly formed, which helps to determine the quality and make sure that bad parts do not make it to the end customer.
6. Machine Vision and Plastic Injection Molding
In plastic injection molding, machine vision can inspect and ensure that the parts being molded are fully formed. The deformed parts could be a result of short shots, meaning not enough material has been inserted into the mold and the part is deformed. This improves quality and reduces waste.