Machine vision, paired with AI and deep learning, expands the role of robots in performing production-line tasks, such as picking, sorting, placing and performing a manufacturing line scan. This combination of technologies also enables robotics to operate in other environments, such as supermarkets, hospitals and restaurants.

Machine vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine vision is similar in complexity to voice recognition.

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Human eyes are sensitive to electromagnetic wavelengths ranging from 390 to 770 nanometers. Video cameras can be sensitive to a range of wavelengths much wider than that. Some machine vision systems function at infrared, ultraviolet or X-ray wavelengths.

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Binocular, also called stereo, machine vision requires a computer with an advanced processor. In addition, high-resolution cameras, a large amount of RAM and AI programming are required for depth perception.

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AI is used in machine vision to expedite the decision-making process. AI can process a large amount of images and data information that was previously too difficult to gather.

Machine vision uses cameras to capture visual information from the surrounding environment. It then processes the images using a combination of hardware and software and prepares the information for use in various applications. Machine vision technology often uses specialized optics to acquire images. This approach lets certain characteristics of the image be processed, analyzed and measured.

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Machine vision is sometimes conflated with the term computer vision. The technology is often integrated with artificial intelligence (AI), machine learning and deep learning to accelerate image processing.

Machine vision is often associated with industrial applications of a computer's ability to see. The term computer vision is often used to describe any technology in which a computer is tasked with digitizing an image, processing the data it contains and taking some kind of action.

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For example, a machine vision application as part of a manufacturing system can be used to analyze a certain characteristic of a part being manufactured on an assembly line. It could determine if the part meets product quality criteria and, if not, dispose of the part.

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In general, the greater the resolution, the more confined the field of vision. Sensitivity and resolution are interdependent. If other factors are constant, increasing the sensitivity reduces the resolution, and increasing the resolution reduces the sensitivity.

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In some cases, the terms machine vision and computer vision are used synonymously. In other cases, distinctions are made.

Computer vision systems collect as much data as possible about objects or scenes and aim to fully understand them. Computer vision is better for collecting general, transferable information that may be applied to a variety of tasks. It also can be performed without a camera as the term can refer to a computer's ability to process images from any source, including the internet. Common applications of computer vision include self-driving cars, reading barcodes and RFID tags, and inspecting for product defects.

Camera lenses vary in optical quality. Two important specifications in any vision system are the sensitivity and the resolution of the lens, which have the following characteristics:

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Another distinction that's often made is in processing power -- that is, the difference between a machine and a computer. A machine vision system typically has less processing power and is used in Lean manufacturing environments, performing practical tasks at a high speed to acquire the data needed to complete a specified job. Quality control, inspection of items and guiding of objects through an assembly line are common applications of machine vision.

Machine vision systems can operate across various dimensions based on the specific needs and requirements of a particular application.

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Machine vision is one of the many applications of AI in manufacturing. Learn other ways manufacturing companies use AI to simplify business processes and increase efficiency.