Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
An intelligent transportation system (ITS) is an advanced application which aims to provide innovative services relating to different modes of transport and traffic management and enable users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks.
Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance.
Forward-looking infrared (FLIR) cameras, typically used on military and civilian aircraft, use a thermographic camera that senses infrared radiation.
Photonics is a branch of optics that involves the application of generation, detection, and manipulation of light in form of photons through emission, transmission, modulation, signal processing, switching, amplification, and sensing. Photonics is closely related to quantum electronics, where quantum electronics deals with the theoretical part of it while photonics deal with its engineering applications. Though covering all light's technical applications over the whole spectrum, most photonic applications are in the range of visible and near-infrared light. The term photonics developed as an outgrowth of the first practical semiconductor light emitters invented in the early 1960s and optical fibers developed in the 1970s.
A thermographic camera is a device that creates an image using infrared (IR) radiation, similar to a normal camera that forms an image using visible light. Instead of the 400–700 nanometre (nm) range of the visible light camera, infrared cameras are sensitive to wavelengths from about 1,000 nm to about 14,000 nm (14 μm). The practice of capturing and analyzing the data they provide is called thermography.
Infrared thermography (IRT), thermal video and/or thermal imaging, is a process where a thermal camera captures and creates an image of an object by using infrared radiation emitted from the object in a process, which are examples of infrared imaging science. Thermographic cameras usually detect radiation in the long-infrared range of the electromagnetic spectrum and produce images of that radiation, called thermograms. Since infrared radiation is emitted by all objects with a temperature above absolute zero according to the black body radiation law, thermography makes it possible to see one's environment with or without visible illumination. The amount of radiation emitted by an object increases with temperature; therefore, thermography allows one to see variations in temperature. When viewed through a thermal imaging camera, warm objects stand out well against cooler backgrounds; humans and other warm-blooded animals become easily visible against the environment, day or night. As a result, thermography is particularly useful to the military and other users of surveillance cameras.
A video camera is an optical instrument that captures videos. Video cameras were initially developed for the television industry but have since become widely used for a variety of other purposes.
Smartdust is a system of many tiny microelectromechanical systems (MEMS) such as sensors, robots, or other devices, that can detect, for example, light, temperature, vibration, magnetism, or chemicals. They are usually operated on a computer network wirelessly and are distributed over some area to perform tasks, usually sensing through radio-frequency identification. Without an antenna of much greater size the range of tiny smart dust communication devices is measured in a few millimeters and they may be vulnerable to electromagnetic disablement and destruction by microwave exposure.
Cognex Corporation is an American manufacturer of machine vision systems, software and sensors used in automated manufacturing to inspect and identify parts, detect defects, verify product assembly, and guide assembly robots. Cognex is headquartered in Natick, Massachusetts, USA and has offices in more than 20 countries.
Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. There are three general types of spectral imagers. There are push broom scanners and the related whisk broom scanners, which read images over time, band sequential scanners, which acquire images of an area at different wavelengths, and snapshot hyperspectral imagers, which uses a staring array to generate an image in an instant.
The following are common definitions related to the machine vision field.
The following outline is provided as an overview of and topical guide to computer vision:
A visual sensor network or smart camera network or intelligent camera network is a network of spatially distributed smart camera devices capable of processing, exchanging data and fusing images of a scene from a variety of viewpoints into some form more useful than the individual images. A visual sensor network may be a type of wireless sensor network, and much of the theory and application of the latter applies to the former. The network generally consists of the cameras themselves, which have some local image processing, communication and storage capabilities, and possibly one or more central computers, where image data from multiple cameras is further processed and fused. Visual sensor networks also provide some high-level services to the user so that the large amount of data can be distilled into information of interest using specific queries.
An under-vehicle inspection (UVI) system generally consists of imaging systems mounted on a roadway and used at facility access points, particularly at secure facilities. An under-vehicle inspection system is used to detect threats—such as bombs—that are hidden underneath vehicles. Cameras capture images of the undercarriage of the vehicle for manual or automated visual inspection by security personnel or systems.
Visual privacy is the relationship between collection and dissemination of visual information, the expectation of privacy, and the legal issues surrounding them. These days digital cameras are ubiquitous. They are one of the most common sensors found in electronic devices, ranging from smartphones to tablets, and laptops to surveillance cams. However, privacy and trust implications surrounding it limit its ability to seamlessly blend into computing environment. In particular, large-scale camera networks have created increasing interest in understanding the advantages and disadvantages of such deployments. It is estimated that over 4 million CCTV cameras deployed in the UK. Due to increasing security concerns, camera networks have continued to proliferate across other countries such as the United States. While the impact of such systems continues to be evaluated, in parallel, tools for controlling how these camera networks are used and modifications to the images and video sent to end-users have been explored.
Optical sorting is the automated process of sorting solid products using cameras and/or lasers.
The Australian Research Centre for Aerospace Automation (ARCAA) was a research centre of the Queensland University of Technology. ARCAA conducted research into all aspects of aviation automation, with a particular research focus on autonomous technologies which support the more efficient and safer utilisation of airspace, and the development of autonomous aircraft and on-board sensor systems for a wide range of commercial applications.
An AI accelerator is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and machine vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2018, a typical AI integrated circuit chip contains billions of MOSFET transistors. A number of vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design.
An event camera, also known as a neuromorphic camera, silicon retina or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional (frame) cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur, and staying silent otherwise.
