Machine Vision vs. Computer Vision: What’s the Difference?

Two key technologies stand out in digital imaging and automation: machine vision and computer vision. Both use visual processing, but their applications and goals differ. Computer vision aims to replicate human vision in computers, allowing them to understand and interpret the visual world. In contrast, machine vision, a branch of computer vision, focuses more on practical uses, especially in industrial and manufacturing settings, where it guides machinery using visual data. Though they share core technologies, their specific applications and impacts across industries underscore their distinct roles.

Learn more about machine vision vs. computer vision, including their uses, advantages, disadvantages, and differences.

What is computer vision? 

Computer vision is a branch of artificial intelligence (AI) that enables computers to gain a high-level understanding and analysis of digital images or videos, similar to the human visual process. It involves the development of algorithms that can process, analyze, and interpret visual data, such as faces, objects, and text, embedded in images and videos. 

Computer vision replicates human vision systems using input from sensing devices, machine learning, AI, and deep learning. Algorithms that train on massive amounts of visual data, or images in the cloud power computer vision applications. They recognize patterns in this visual data and use those patterns to determine the content of other images.  

Through machine learning, computers learn to identify patterns, such as shapes and colors, in visual data. Once trained, they can then recognize and classify objects in new images. With increased exposure to more data, accuracy improves. 

What is computer vision used for? 

The purpose of computer vision is to help a computer imitate the human vision system so that the computer can automate tasks that humans can do with greater reliability and accuracy. As a result, many applications, including facial recognition systems, autonomous vehicles, augmented reality, and health diagnostics, often use computer vision technology. Learn more about a few different types of computer vision applications:

• Agriculture: Computer vision analyzes images and drone footage of crops to monitor harvests and detect issues that could affect a healthy harvest. Vertical farming, growing crops vertically indoors versus outdoors, also benefits from computer vision. It enables observing a plant’s life cycle, allowing farmers to grow indoors for faster, more persistent, and healthier crop production.

• Sports: To aid coaches and sports analysts in making predictions and determining strategies, computer vision can track and detect objects in gameplay.  Coaches can also monitor players to identify patterns in their behavior, helping them to place players in optimal positions during gameplay. 

• Manufacturing: Computer vision helps manufacturers better monitor and adhere to safety protocols as a means of quality assurance. The technology also lends itself to other helpful uses in the manufacturing process, including inventory management and product design. 

• Transportation: From managing traffic flow to detecting moving violations, computer vision helps law enforcement agencies keep the roads safer and easier to navigate. Computer vision will stand as a core component of advanced driver assistance systems (ADAS), with live streaming and dash cameras providing computer vision-powered video analysis to take action when needed. These advances can lead to increased safety on the roadways.

• Health care: Computer vision can be integral to disease detection, diagnosis, and monitoring. When used in radiology, this type of machine learning allows providers to better track tumor growth. Using computer vision, it’s possible to reconstruct images from low-resolution computed tomography (CT) images into high-resolution images so that providers have better information to work with when devising a care plan. 

Advantages of computer vision 

People can harness the power of computers through computer vision to improve the capabilities of humankind’s visual processing systems. Depending on the application, the advantages of computer vision are vast. For instance, it can provide a safer working environment for employees, greater accuracy in detecting flaws in manufactured products, enhanced public safety and security, and earlier detection and enhanced monitoring of pathology in the body. 

The benefits of automation provide advantages in computer vision. Computer vision facilitates automation and efficiency in processing and interpreting visual data. For example, computer vision makes automated retail checkout possible with shorter wait times and a better customer experience. Computer vision can also automate the insurance claims process, making it more efficient and accurate for both the claimant and the insurance company. Computer vision bots can even verify the identity of the claimants as a means of fraud prevention.

Automation also leads to better operational efficiency. For instance, business owners can use the technology to authenticate employees with automatic facial recognition and catch product quality defects before delivering products to customers. 

Overall, computer vision drives innovation in AI, enhancing recognition, tracking, and analysis capabilities. It’s also more accessible today and requires less computing power, allowing more organizations to rely on vision applications to improve the accuracy of image data processing for various purposes. 

Disadvantages of computer vision 

The disadvantages of computer vision center around the challenges and costs of computational resources. Additional drawbacks involve the nuances of navigating more dynamic environments and the delicate and evolving relationship between computer vision versus human vision. 

For some organizations, computer vision costs are too high, especially given the need for human expertise in AI, machine learning, and deep learning. In many cases, ongoing monitoring and tweaking of the computer vision system is necessary for troubleshooting and evaluating its efficiency and efficacy. 

Privacy and security stand as other potential considerations. Security and privacy breaches and hackers taking over a computer vision system could negatively affect the public and organizations. In some applications, such as health care, ethical and privacy concerns may arise where protection of patient information and sensitivity to the patient experience need further consideration. 

In general, the greatest consideration for computer vision is the recognition of the limitations and “blind spots” that naturally exist using this technology. Computer vision systems are only as good as the data on which they are trained, which means it’s essential that they train on a wide variety of data sets. Computers also lack the context humans naturally bring to the vision processing system. The inability to weigh the importance of objects within surroundings is a shortcoming of computer vision systems that can lead to inaccuracies. 

What is machine vision? 

Machine vision refers to the use of computer vision in industrial and manufacturing contexts to control or guide machinery. Machine vision systems use digital sensors located within specialized industrial cameras to acquire images so that computer software and hardware can analyze, process, and measure a variety of object characteristics. As an essential technology of industrial automation, it combines software and hardware to work in unison, capturing and processing images to automate tasks. 

Machine vision acts as the eyes to “see” what certain industrial equipment is doing. The goal is to make informed decisions based on the equipment's actions. This allows for real-time adjustments to equipment parameters, including part positioning, defect detection, and product tracking. 

What is machine vision used for? 

Machine vision is a main component of industrial automation. Primarily, quality control, inspection, and robotic guidance within production lines rely on machine vision systems.

Machines equipped with smart vision can use natural language processing to complete tasks. These tasks include reading and interpreting labels and signs, packing shipping containers and boxes, and calculating volumes. Beyond visual inspection and quality control, robots with smart machine vision can also interact with each other and human coworkers to ensure the safety and accuracy of the production process.

As such, automotive, electronics, and packaging industries use machine vision to enhance precision, efficiency, and productivity.

Advantages of machine vision 

Using AI technologies, machine vision allows humans to optimize and automate elements of industrial production like never before. As a result, the industries that use machine vision experience new levels of industrial innovation that lead to an overall smarter manufacturing process, from logistics to operations, safety to optimization, and beyond. 

Improved product quality is a major advantage of machine vision. Machine vision can increase production efficiency by automating inspection and quality control, producing a more efficient and accurate manufacturing system. For organizations, product quality is a cornerstone of business operations. Companies can automate the laborious and tedious quality control process using machine learning while freeing employees to focus on other tasks. Moreover, it ensures customer satisfaction and safety. 

Organizations may see cost savings and less waste when employing machine vision in the manufacturing process. Machine vision allows for higher measurement accuracy that robots can use when packing and shipping materials. Product defects have a greater chance of being caught with machine vision at earlier stages of the production process. These capabilities not only save time for organizations but also money.

Increased human safety is perhaps a top advantage of machine vision. Since machine vision reduces the need for human involvement, it provides additional safety from hazardous environments and operational benefits for human workers. 

Disadvantages of machine vision 

Considerations and disadvantages of machine vision include potentially high initial costs, hardware hindrances, a lack of understanding of its true capabilities, restrictions of traditional algorithms, and some of computer systems' natural limitations. 

Machine vision technologies are initially quite costly. To implement this type of AI-driven technology, you must purchase expensive components, including sensors, cameras, specialized software, and a robust infrastructure to support the technology. Initial costs can be high; however, many organizations see machine vision technologies as a long-term investment. The wrong type of hardware, hardware malfunctions, and other issues with the sensors and cameras of a machine vision system can lead to costly and negative outcomes. 

Explainability in machine learning refers to understanding how models make decisions, and it’s a crucial aspect of machine vision because AI systems can sometimes make errors or rely on misleading patterns. For example, minor metadata changes in an image can cause a machine to misidentify objects, or an algorithm might base medical predictions on irrelevant factors, such as using older X-ray machines to infer disease prevalence. Understanding and verifying the rules behind AI decisions helps make its processes clear. On the other hand, a lack of understanding can undermine machine vision systems' reliability, safety, and fairness.

Traditional machine vision systems use rule-based algorithms that work well with consistent, high-quality parts. However, programming these systems becomes challenging as exceptions and defect variations increase. They struggle with complex inspections involving unpredictable defects, scale changes, and slight part variations, particularly when dealing with subtle differences between functional and cosmetic flaws. Factors such as lighting, color changes, and curvature can complicate inspections, which can be problematic.

As with any deep learning tool, machine vision systems lack certain abilities that humans possess.. These natural differences arise when working with AI tools like machine vision systems. For example, conceptual understanding is often lacking when using machine vision in various applications. 

Other considerations 

When considering a career or educational path in these fields, think about your interest in practical applications versus theoretical research. Machine vision offers a more hands-on approach with immediate industrial applications. Meanwhile, computer vision spans various possibilities, from research to product development. Familiarizing yourself with both can significantly enhance your versatility and value in tech-driven industries.

Getting started with computer and machine vision on Coursera 

Exploring Coursera courses on computer and machine vision offers foundational knowledge in these technologies, preparing you for roles in AI, robotics, and software development.

Are you familiar with computer vision and want to learn a skill, such as how to train and develop an image classification system using machine learning? Consider enrolling in Computer Vision with Embedded Machine Learning, offered by Edge Impulse and available on Coursera. MathWorks also has an online course on Coursera, Computer Vision for Engineering and Science Specialization, which is ideal for anyone who wants to gain skills in computer vision, such as detecting objects, aligning images, and tracking motion. Enroll today.