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 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 definition
Computer vision is a subfield of artificial intelligence that enables machines to perceive, analyse, and extract meaning from visual data such as images and video. Using deep learning and neural networks, computer vision systems identify patterns, recognise 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.”
How does computer vision work?
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.
Image acquisition and pre-processing
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.
Feature extraction
At this stage, the system detects basic image features like edges, colours, patterns, or motion. Instead of analysing raw pixels, it uses simplified numerical values to describe what’s present and how it changes over time.
Object detection and classification
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.
Image classification
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 categorised as a “defective part” or a photo as “pallet full.”
Object tracking
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 behaviour.
Core computer vision technologies
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 recognise specific objects by training on massive datasets of labelled images. Early training might involve distinguishing cars from other vehicles, then identifying different types of cars, and eventually recognising 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.
- Convolutional neural networks (CNNs)
A convolutional neural network applies small filters across the input image to detect specified patterns, such as textures or shapes. These patterns are then passed through multiple neural layers, handling increasingly complex features at each step. Facial recognition is an example of this. - Deep learning and neural networks
A weight is a degree of value that a deep learning model assigns to a piece of information or to the neural pathways within its own network. As it learns from these images, it begins to adjust these weights to reflect its growing awareness of patterns and relevant details. - Traditional image processing
Classic analytical tools are still in use for things like motion detection, image cleanup, or basic pattern detection such as barcode reading. These older methods are economical and are increasingly used in a hybrid fashion with deep learning tools. - Frameworks and libraries
Computer vision is supported by vast libraries of images, algorithms, and training frameworks for deep learning models. Some of these tools are open source and some are proprietary, based upon the complexity of the industries in which they are used.