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By eliminating human involvement in production processes, machine vision adds significant safety and operational benefits.
FREMONT, CA: Machine vision is a technique that utilizes image processing to enable automatic inspection and analysis for applications such as automatic inspection, process control, and robotic guiding. It's critical to understand that when people talk about machine vision, they're referring to a broad range of technologies, software and hardware products, integrated systems, activities, methodologies, and knowledge.
Machine vision is a technical competence that extends the capabilities of existing technologies and utilizes them in novel ways to solve real-world issues.
Machine vision is a branch of systems engineering that can be distinguished from computer vision, a branch of computer science that does not involve a physical piece of hardware such as a vision box or camera mounted on a robot.
Machine vision is the physical structure of a system. In contrast, computer vision is the system's intelligence, much as how a computer serves as a container for the components inside, such as the computer chips that run the computer.
Without computer vision, machine vision cannot function, as it is the brains that process the data. It's critical to remember that as computer vision technology progresses, the number of potential applications for machine vision expands proportionately. ClearView Imaging provides a compelling case that computer vision can handle images that are not photographs or films generated by a thermal or infrared sensor, a motion detector, or other sources.
Machine vision systems have been in use since the 1950s, but it wasn't until the 1980s and 1990 that the technology indeed took off and gained popularity.
Machine vision is gaining traction in industrial automation contexts and other sectors such as security, autonomous cars, food processing, packaging, and logistics, as well as in robots and drones.
Machine vision can be combined with other technologies such as deep learning and machine learning to assist businesses in better understanding their data and optimizing their operations for increased efficiency, as demonstrated by BMW's use of the technology in conjunction with AI and machine learning.
From its inception in the 1950s, through the introduction of the first machine vision course at MIT in the 1970s, to its numerous applications today, machine vision has evolved from a conceptual aspect of computer science to a critical part of manufacturing. The most advanced systems provide very versatile solutions that assist in identifying flaws, sorting products, and doing a range of activities more rapidly and efficiently than humans ever could.
Today, every organization dependent on creating things to a precise standard may benefit from incorporating machine vision into its manufacturing process. Any piece of intricate equipment, whether used in medical devices or sensors that end up in a car or plane, requires extreme precision and reliability: there is no room for error.
Industries with strict regulatory requirements, such as giant pharma, increasingly rely on machine vision to detect irregularities in their medicinal products. Indeed, once the new EU Good Manufacturing Practice (GMP) requirements are finalized, human inspection will be prohibited.
With risk minimization being a primary objective in the pharmaceutical industry, machine vision will likely play an increasing role in mitigating the hazards associated with the human mistake.