Computer vision is the study and development of methods for extracting information from images, as well as systems which mimic human visual perception so computers can make data-based decisions and complete tasks efficiently.
Computer vision is not a single science; rather it encompasses multiple disciplines and technologies used in its design, development and application. While one part of computer vision, called theoretical computer vision, studies the theoretical foundations that lie behind artificial systems designed to extract information from image data sources, another focusses on applying these theories when developing computer vision systems.
Computer vision seeks to understand the content of digital images, such as objects, text descriptions and 3D models. This may involve recognizing an object from video footage or detecting its presence; searching through catalogues for images matching specific descriptions; or simply recognising an image from memory.
In many instances, this task can be achieved using machine learning - an artificial intelligence technique which trains systems to recognize specific objects or patterns within an image - which compares it with previously seen images from a large database.
Different industries employ computer vision technology to enhance consumer experiences, reduce costs and bolster security. Retailers notably utilize computer vision for speedier checkout, out-of-stock product detection and loss prevention measures.
Public sector agencies rely on computer vision technology to monitor equipment and infrastructure, including safety hazards, defects, and potential violations of regulations.
Self-driving cars are another application of computer vision technology. These cars use live object scans, categorizing features based on them and making data-based decisions instantly - all within seconds!
One of the primary obstacles of computer vision is that it requires an enormous database, as it must process thousands of visual reference inputs before making decisions effectively. Once considered a summer student project in the 1960s, today computer vision has become useful to computers as part of everyday computing systems.
Neural networks lie at the core of computer vision. Like a giant puzzle piece jigsaw puzzle, neural networks assemble all the pieces of an image through filtering and actions in deep network layers before finally reconstituting it as its final form.
Convolutional neural networks (CNNs) can transform pixels of an image into lines, which are then combined into features like eyes and faces. If necessary, machine learning techniques may also be utilized in order to learn additional complex items like facial shape, age and gender.
Google Translate and Lens are examples of applications which use computer vision technology to instantly translate over 100 languages using deep learning technology. Face recognition, which uses computer vision technology to identify people and their expressions in photos, is another common use.
Facial recognition technology has quickly emerged as a valuable security solution for both businesses and personal electronics, providing protection from theft as well as monitoring access to various types of devices such as smartphones or tablets.
