What is computer vision?
Computer vision helps systems interpret images and video in real time. From quality control to safety monitoring, it turns visual input into fast, reliable decisions that improve performance across industries.
Computer vision helps systems interpret images and video in real time. From quality control to safety monitoring, it turns visual input into fast, reliable decisions that improve performance across industries.
Computer vision has been around for decades, quietly powering things like barcode scanners, motion detectors, or traffic monitors. But with the explosive rise of AI and machine learning, what used to be a set of hand-coded rules is now a dynamic system that can learn, adapt, and improve with every image it sees. Today’s computer vision does so much more than simply detecting what’s there. It understands context, tracks changes, and can integrate with business systems in real time to power smart automations and fast decisions. From warehouse cameras to surgical tools, it’s giving businesses a new way to see – and act on – the world around them.
Computer vision is a subfield of artificial intelligence that enables machines to perceive, analyze, and extract meaning from visual data such as images and video. Using deep learning and neural networks, computer vision systems identify patterns, recognize objects, and infer relationships between them. They can segment scenes, detect anomalies in movement, read text, and much more – triggering automated actions based on what they “see.”
Computer vision turns raw visual input into meaningful insights. Like human vision, it begins with raw data and moves through stages of interpretation. Instead of neurons, it uses deep learning and image processing to understand what it sees and trigger appropriate actions. Below are the key stages in a typical computer vision pipeline.
Computer vision systems begin with raw input such as images or video from nearly any source. Before analysis, the data is cleaned and enhanced to reduce noise, improve quality, and include infrared or thermal signals.
At this stage, the system detects basic image features like edges, colors, patterns, or motion. Instead of analyzing raw pixels, it uses simplified numerical values to describe what’s present and how it changes over time.
The system identifies and locates objects in relation to the camera and to each other. By learning from thousands of examples, it can distinguish people, vehicles, packages, or equipment – even in cluttered or fast-moving scenes.
Rather than simply identifying specific objects, classification training lets models assign a label to the entire image or frame as to what “kind” of thing it exemplifies. For example, a scan may be categorized as a “defective part” or a photo as “pallet full.”
This is the detection and measurement of object motion over multiple frames of input video. It is especially useful in scenarios with vehicular or workplace safety issues as it can reveal essential context such as direction, speed, or behavior.
Modern computer vision solutions rely on deep learning – a more advanced form of machine learning that uses layered neural networks, much like the structure of the human brain. With this capability, systems can automatically learn to detect edges, track motion, and recognize specific objects by training on massive datasets of labeled images. Early training might involve distinguishing cars from other vehicles, then identifying different types of cars, and eventually recognizing individual parts and even subtle variations within those parts. Thanks to AI, computer vision has evolved from a helpful tool into a vital, irreplaceable part of many business operations.
Computer vision requires all the core components of artificial intelligence to work together. Each of these layers plays a distinct role in powering modern vision systems to do what they do:
AI is the broadest category and refers to any technology that is designed to simulate human intelligence. Just as natural language processing models allow AI systems to “understand” human speech, computer vision lets them “see” and interpret visual information.
Machine learning is a subset of AI that lets models learn directly from data. It helps computer vision systems recognize patterns in visual inputs and distinguish between different objects or behaviors based on previous examples. Over time, models improve as they are exposed to more data.
Deep learning is a specialized approach within ML that uses artificial neural networks with many layers to interpret complex, unstructured data. It allows computer vision systems to move beyond basic pattern recognition and perform more nuanced tasks , such as identifying specific defects on a product line .
Machine vision specifically refers to industrial systems that use cameras and sensors to inspect, measure, or guide machinery. It's usually hardware-focused and tightly integrated with manufacturing equipment like robotic arms, conveyor belts, or assembly lines. The goal is to automate and speed up production by checking for quality and consistency issues. Unlike computer vision, machine vision doesn’t use AI or learn from data. It relies instead on fixed rules and controlled conditions to perform predefined tasks.
While modern computer vision has so many amazing capabilities, it can be hard to contextualize those uses without some specific examples. The functionalities below have uses across different types of operations and represent some of computer vision’s more common tasks:
Today’s computer vision technologies have evolved to the extent where they are becoming indispensable in a number of industries. Below are just a few examples of computer vision use cases in some core sectors:
Computer vision in automotive verifies that sensors and control units are correctly installed and free from damage. It inspects welds, alignment, connector seating, and surface finishes at high speed. In EV manufacturing, vision tools can rapidly check a range of complex electronic and battery issues.
Computer vision in distribution works alongside automated conveyor systems to identify package destinations and trigger lane-switching mechanisms for accurate cross-dock sorting. Vision systems also monitor damaged cartons or other anomalies and flag them before they’re scanned into inventory.
Computer vision in F&B tracks fill levels and checks that caps or seals are properly secured. In packaging areas, computer vision systems inspect seals for gaps or defects and scan foreign objects on conveyor belts. These tools also confirm that labels are legible and accurate before goods leave the facility.
Computer vision in healthcare monitors surgical instrument trays to ensure all tools are present and sterile. Smart overhead cameras flag missing items or protocol deviations before surgery begins. They support pathology by visually scanning slide images and flagging cells or tissues for further review.
Computer vision in retail checks for signs of wear or damage, helping staff make accurate restock or disposal decisions. It can match visual cues on items to pick lists, reducing mis-ships and improving customer satisfaction. And it can help analyze checkout areas for bottlenecks and merchandising compliance.
Modern AI-powered solutions are nothing short of awe-inspiring with their ability to learn and reason things out at such scale and speed. But it's important to remember that they are tools to augment human knowledge and discretion – not magical robots to replace them. The best and most amazing results will come from pairing your teams with powerful AI toolkits, and giving them the support and guidance they need to tackle common challenges like these:
The power of computer vision is its amazing ability to turn simple pixels into actual insight. By giving machines the ability to interpret the visual world, they can take things like photos, video, or sensor feeds and derive actionable insights – often in the blink of an eye. As the tools become more accessible, teams across industries are finding new ways to reduce errors, respond faster, and unlock cross-business visibility.
See how Infor’s AI solutions support computer vision capabilities – from quality control and safety monitoring to label compliance and beyond.