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Computer vision is an interdisciplinary scientific field that deals with how
computer A computer is a machine that can be programmed to carry out sequences of arithmetic or logical operations automatically. Modern computers can perform generic sets of operations known as Computer program, programs. These programs enable compu ...

computer
s can gain high-level understanding from
digital image A digital image is an composed of s, also known as ''pixels'', each with ', ' of numeric representation for its or that is an output from its fed as input by its denoted with ''x'', ''y'' on the x-axis and y-axis, respectively. Depending on ...
s or
video Video is an electronic Electronic may refer to: *Electronics Electronics comprises the physics, engineering, technology and applications that deal with the emission, flow and control of electrons in vacuum and matter. It uses active de ...

video
s. From the perspective of
engineering Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more specializ ...

engineering
, it seeks to understand and automate tasks that the
human visual system The visual system comprises the sensory organ (the eye) and parts of the central nervous system (the retina The retina (from la, rete) is the innermost, light-sensitive layer of tissue of the eye of most vertebrate Vertebrates () com ...
can do. Computer vision tasks include methods for acquiring, processing,
analyzing Analysis is the process of breaking a complexity, complex topic or Substance theory, substance into smaller parts in order to gain a better understanding of it. The technique has been applied in the study of mathematics and logic since before Ari ...
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 (the input of the retina) 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. The
scientific discipline The branches of science, also referred to as science Science (from the Latin word ''scientia'', meaning "knowledge") is a systematic enterprise that Scientific method, builds and Taxonomy (general), organizes knowledge in the form of Tes ...
of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning device. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems. Sub-domains of computer vision include scene reconstruction,
object detection Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched ...
, event detection,
video tracking Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression Compr ...
,
object recognition The following outline is provided as an overview of and topical guide to object recognition: Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recogn ...
,
3D pose estimation 3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan. It arises in computer vision or robotics Robotics is an interdisciplinary Interdisciplinarity ...
, learning, indexing,
motion estimation Image:Elephantsdream_vectorstill06.png, 350px, Motion vectors that result from a movement into the z-plane of the image, combined with a lateral movement to the lower-right. This is a visualization of the motion estimation performed in order to comp ...
, visual servoing, 3D scene modeling, and
image restoration Image restoration is the operation of taking a corrupt/noisy image and estimating the clean, original image. Corruption may come in many forms such as motion blur Motion blur is the apparent streaking of moving objects in a photograph or a sequen ...
.


Definition

Computer vision is an
interdisciplinary field Interdisciplinarity or interdisciplinary studies involves the combination of two or more academic disciplines into one activity (e.g., a research project). It draws knowledge from several other fields like sociology, anthropology, psychology, e ...
that deals with how computers and can be made to gain high-level understanding from
digital image A digital image is an composed of s, also known as ''pixels'', each with ', ' of numeric representation for its or that is an output from its fed as input by its denoted with ''x'', ''y'' on the x-axis and y-axis, respectively. Depending on ...
s or
video Video is an electronic Electronic may refer to: *Electronics Electronics comprises the physics, engineering, technology and applications that deal with the emission, flow and control of electrons in vacuum and matter. It uses active de ...

video
s. From the perspective of
engineering Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more specializ ...

engineering
, it seeks to automate tasks that the
human visual system The visual system comprises the sensory organ (the eye) and parts of the central nervous system (the retina The retina (from la, rete) is the innermost, light-sensitive layer of tissue of the eye of most vertebrate Vertebrates () com ...
can do. "Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding."http://www.bmva.org/visionoverview The British Machine Vision Association and Society for Pattern Recognition Retrieved February 20, 2017 As a
scientific discipline The branches of science, also referred to as science Science (from the Latin word ''scientia'', meaning "knowledge") is a systematic enterprise that Scientific method, builds and Taxonomy (general), organizes knowledge in the form of Tes ...
, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.


History

In the late 1960s, computer vision began at universities which were pioneering
artificial intelligence Artificial intelligence (AI) is intelligence Intelligence has been defined in many ways: the capacity for logic Logic (from Ancient Greek, Greek: grc, wikt:λογική, λογική, label=none, lit=possessed of reason, intellectual, ...

artificial intelligence
. It was meant to mimic the
human visual system The visual system comprises the sensory organ (the eye) and parts of the central nervous system (the retina The retina (from la, rete) is the innermost, light-sensitive layer of tissue of the eye of most vertebrate Vertebrates () com ...
, as a stepping stone to endowing robots with intelligent behavior. In 1966, it was believed that this could be achieved through a summer project, by attaching a camera to a computer and having it "describe what it saw". What distinguished computer vision from the prevalent field of
digital image processing Digital image processing is the use of a digital computer A computer is a machine A machine is a man-made device that uses power to apply forces and control movement to perform an action. Machines can be driven by animals and people ...
at that time was a desire to extract
three-dimensional Three-dimensional space (also: 3-space or, rarely, tri-dimensional space) is a geometric setting in which three values (called parameter A parameter (from the Ancient Greek language, Ancient Greek wikt:παρά#Ancient Greek, παρά, ''par ...
structure from images with the goal of achieving full scene understanding. Studies in the 1970s formed the early foundations for many of the computer vision
algorithm In and , an algorithm () is a finite sequence of , computer-implementable instructions, typically to solve a class of problems or to perform a computation. Algorithms are always and are used as specifications for performing s, , , and other ...

algorithm
s that exist today, including from images, labeling of lines, non-polyhedral and polyhedral modeling, representation of objects as interconnections of smaller structures,
optical flow Optical flow or optic flow is the pattern of apparent motion Image:Leaving Yongsan Station.jpg, 300px, Motion involves a change in position In physics, motion is the phenomenon in which an object changes its position (mathematics), position over ...
, and
motion estimation Image:Elephantsdream_vectorstill06.png, 350px, Motion vectors that result from a movement into the z-plane of the image, combined with a lateral movement to the lower-right. This is a visualization of the motion estimation performed in order to comp ...
. The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision. These include the concept of scale-space, the inference of shape from various cues such as
shading Shading refers to the depiction of depth perception in 3D models (within the field of 3D computer graphics) or illustrations (in visual art) by varying the level of darkness. Shading tries to approximate local behavior of light on the object's s ...

shading
, texture and focus, and contour models known as snakes. Researchers also realized that many of these mathematical concepts could be treated within the same optimization framework as
regularization Regularization may refer to: * Regularization (linguistics) * Regularization (mathematics) * Regularization (physics) * Regularization (solid modeling) * Regularization Law, an Israeli law purporting to retroactively legalize settlements See also ...
and
Markov random field In the domain of physics Physics (from grc, φυσική (ἐπιστήμη), physikḗ (epistḗmē), knowledge of nature, from ''phýsis'' 'nature'), , is the natural science that studies matter, its Motion (physics), motion and behav ...
s. By the 1990s, some of the previous research topics became more active than the others. Research in projective 3-D reconstructions led to better understanding of
camera calibrationCamera calibration may refer to: *Camera resectioning, which is called also geometric camera calibration *Color mapping, which is a method for photometric camera calibration *Radiometric calibration {{disambig ...
. With the advent of optimization methods for camera calibration, it was realized that a lot of the ideas were already explored in
bundle adjustment Given a set of images depicting a number of 3D points from stereoscopy, different viewpoints, bundle adjustment can be defined as the problem of simultaneously refining the 3D Coordinate system, coordinates describing the scene geometry, the paramet ...
theory from the field of
photogrammetry Photogrammetry is the science and technology of obtaining reliable information about physical objects and the environment through the process of recording, measuring and interpreting photographic images and patterns of electromagnetic radiant imag ...
. This led to methods for sparse 3-D reconstructions of scenes from multiple images. Progress was made on the dense stereo correspondence problem and further multi-view stereo techniques. At the same time, variations of graph cut were used to solve
image segmentation In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (Set (mathematics), sets of pixels). The goal of segmenta ...

image segmentation
. This decade also marked the first time statistical learning techniques were used in practice to recognize faces in images (see
Eigenface Some eigenfaces from AT&T Labs, AT&T Laboratories Cambridge An eigenface () is the name given to a set of eigenvectors when used in the computer vision problem of human facial recognition system, face recognition. The approach of using eigenfaces f ...
). Toward the end of the 1990s, a significant change came about with the increased interaction between the fields of
computer graphics Computer graphics deals with generating images with the aid of computers A computer is a machine that can be programmed to Execution (computing), carry out sequences of arithmetic or logical operations automatically. Modern computers can p ...
and computer vision. This included image-based rendering, image morphing, view interpolation, panoramic image stitching and early light-field rendering. Recent work has seen the resurgence of
feature Feature may refer to: Computing * Feature (CAD), could be a hole, pocket, or notch * Feature (computer vision), could be an edge, corner or blob * Feature (software design) is an intentional distinguishing characteristic of a software item ( ...
-based methods, used in conjunction with machine learning techniques and complex optimization frameworks. The advancement of Deep Learning techniques has brought further life to the field of computer vision. The accuracy of deep learning algorithms on several benchmark computer vision data sets for tasks ranging from classification, segmentation and optical flow has surpassed prior methods.


Related fields


Solid-state physics

Solid-state physics Solid-state physics is the study of rigid matter, or solids, through methods such as quantum mechanics, crystallography, electromagnetism, and metallurgy. It is the largest branch of condensed matter physics. Solid-state physics studies how the la ...
is another field that is closely related to computer vision. Most computer vision systems rely on
image sensors An image sensor or imager is a sensor that detects and conveys information used to make an image. It does so by converting the variable attenuation of light waves (as they refraction, pass through or reflection (physics), reflect off objects) int ...
, which detect
electromagnetic radiation In physics Physics is the natural science that studies matter, its Elementary particle, fundamental constituents, its Motion (physics), motion and behavior through Spacetime, space and time, and the related entities of energy and force. ...

electromagnetic radiation
, which is typically in the form of either
visible Visibility is in meteorology, a measure of the distance at which an object or light can be seen. Visibility may also refer to: * Visual perception ** Naked-eye visibility * A measure of turbidity in water quality control * Interferometric visibili ...
or
infrared light Infrared (IR), sometimes called infrared light, is electromagnetic radiation In physics Physics (from grc, φυσική (ἐπιστήμη), physikḗ (epistḗmē), knowledge of nature, from ''phýsis'' 'nature'), , is the natur ...
. The sensors are designed using
quantum physics Quantum mechanics is a fundamental theory A theory is a reason, rational type of abstraction, abstract thinking about a phenomenon, or the results of such thinking. The process of contemplative and rational thinking is often associated with ...
. The process by which light interacts with surfaces is explained using physics. Physics explains the behavior of
optics Optics is the branch of that studies the behaviour and properties of , including its interactions with and the construction of that use or it. Optics usually describes the behaviour of , , and light. Because light is an , other forms of s ...

optics
which are a core part of most imaging systems. Sophisticated
image sensors An image sensor or imager is a sensor that detects and conveys information used to make an image. It does so by converting the variable attenuation of light waves (as they refraction, pass through or reflection (physics), reflect off objects) int ...
even require
quantum mechanics Quantum mechanics is a fundamental theory A theory is a reason, rational type of abstraction, abstract thinking about a phenomenon, or the results of such thinking. The process of contemplative and rational thinking is often associated with ...
to provide a complete understanding of the image formation process. Also, various measurement problems in physics can be addressed using computer vision, for example motion in fluids.


Neurobiology

Neurobiology Neuroscience is the scientific study of the nervous system In Biology, biology, the nervous system is a Complex system, highly complex part of an animal that coordinates its Behavior, actions and Sense, sensory information by transmitting ...
, specifically the study of the biological vision system. Over the last century, there has been an extensive study of eyes, neurons, and the brain structures devoted to processing of visual stimuli in both humans and various animals. This has led to a coarse, yet complicated, description of how "real" vision systems operate in order to solve certain vision-related tasks. These results have led to a sub-field within computer vision where artificial systems are designed to mimic the processing and behavior of biological systems, at different levels of complexity. Also, some of the learning-based methods developed within computer vision (''e.g.'' and
deep learning#REDIRECT Deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-super ...

deep learning
based image and feature analysis and classification) have their background in biology. Some strands of computer vision research are closely related to the study of
biological vision Visual perception is the ability to interpret the surrounding environment using light in the visible spectrum reflected by the objects in the environment (biophysical), environment. This is different from visual acuity, which refers to how clea ...
– indeed, just as many strands of AI research are closely tied with research into human consciousness, and the use of stored knowledge to interpret, integrate and utilize visual information. The field of biological vision studies and models the physiological processes behind visual perception in humans and other animals. Computer vision, on the other hand, studies and describes the processes implemented in software and hardware behind artificial vision systems. Interdisciplinary exchange between biological and computer vision has proven fruitful for both fields.


Signal processing

Yet another field related to computer vision is
signal processing Signal processing is an electrical engineering Electrical engineering is an engineering discipline concerned with the study, design, and application of equipment, devices, and systems which use electricity, electronics, and electromagnetis ...

signal processing
. Many methods for processing of one-variable signals, typically temporal signals, can be extended in a natural way to processing of two-variable signals or multi-variable signals in computer vision. However, because of the specific nature of images there are many methods developed within computer vision that have no counterpart in processing of one-variable signals. Together with the multi-dimensionality of the signal, this defines a subfield in signal processing as a part of computer vision.


Robotic navigation

Robot navigation Robot localization denotes the robot's ability to establish its own position and orientation within the frame of reference In physics Physics is the natural science that studies matter, its Elementary particle, fundamental constituents, ...
sometimes deals with autonomous path planning or deliberation for robotic systems to navigate through an environment. A detailed understanding of these environments is required to navigate through them. Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot.


Other fields

Beside the above-mentioned views on computer vision, many of the related research topics can also be studied from a purely mathematical point of view. For example, many methods in computer vision are based on
statistics Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical ...

statistics
,
optimization File:Nelder-Mead Simionescu.gif, Nelder-Mead minimum search of Test functions for optimization, Simionescu's function. Simplex vertices are ordered by their values, with 1 having the lowest ( best) value., alt= Mathematical optimization (alter ...
or
geometry Geometry (from the grc, γεωμετρία; ' "earth", ' "measurement") is, with , one of the oldest branches of . It is concerned with properties of space that are related with distance, shape, size, and relative position of figures. A mat ...

geometry
. Finally, a significant part of the field is devoted to the implementation aspect of computer vision; how existing methods can be realized in various combinations of software and hardware, or how these methods can be modified in order to gain processing speed without losing too much performance. Computer vision is also used in fashion ecommerce, inventory management, patent search, furniture, and the beauty industry.


Distinctions

The fields most closely related to computer vision are
image processing Digital image processing is the use of a digital computer A computer is a machine A machine is a man-made device that uses power to apply forces and control movement to perform an action. Machines can be driven by animals and people ...
,
image analysis Image analysis or imagery analysis is the extraction of meaningful information from image File:TEIDE.JPG, An Synthetic aperture radar, SAR radar imaging, radar image acquired by the SIR-C/X-SAR radar on board the Space Shuttle Endeavour sho ...
and
machine vision 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 ma ...

machine vision
. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented. In image processing, the input is an image and the output is an image as well, whereas in computer vision, an image or a video is taken as an input and the output could be an enhanced image, an understanding of the content of an image or even a behaviour of a computer system based on such understanding.
Computer graphics Computer graphics deals with generating images with the aid of computers A computer is a machine that can be programmed to Execution (computing), carry out sequences of arithmetic or logical operations automatically. Modern computers can p ...

Computer graphics
produces image data from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, ''e.g.'', as explored in
augmented reality Augmented reality (AR) is an interactive experience of a real-world environment where the objects that reside in the real world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including v ...
. The following characterizations appear relevant but should not be taken as universally accepted: *
Image processing Digital image processing is the use of a digital computer A computer is a machine A machine is a man-made device that uses power to apply forces and control movement to perform an action. Machines can be driven by animals and people ...
and
image analysis Image analysis or imagery analysis is the extraction of meaningful information from image File:TEIDE.JPG, An Synthetic aperture radar, SAR radar imaging, radar image acquired by the SIR-C/X-SAR radar on board the Space Shuttle Endeavour sho ...
tend to focus on 2D images, how to transform one image to another, ''e.g.'', by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither require assumptions nor produce interpretations about the image content. * Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several images, ''e.g.'', how to reconstruct structure or other information about the 3D scene from one or several images. Computer vision often relies on more or less complex assumptions about the scene depicted in an image. *
Machine vision 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 ma ...

Machine vision
is the process of applying a range of technologies & methods to provide imaging-based automatic inspection, process control and robot guidance in industrial applications. Machine vision tends to focus on applications, mainly in manufacturing, ''e.g.'', vision-based robots and systems for vision-based inspection, measurement, or picking (such as bin picking). This implies that image sensor technologies and control theory often are integrated with the processing of image data to control a robot and that real-time processing is emphasised by means of efficient implementations in hardware and software. It also implies that the external conditions such as lighting can be and are often more controlled in machine vision than they are in general computer vision, which can enable the use of different algorithms. * There is also a field called
imaging Imaging is the representation or reproduction of an object's form; especially a visual representation (i.e., the formation of an image). Imaging technology is the application of materials and methods to create, preserve, or duplicate images. I ...
which primarily focuses on the process of producing images, but sometimes also deals with processing and analysis of images. For example,
medical imaging Medical imaging is the technique and process of imaging Imaging is the representation or reproduction of an object's form; especially a visual representation (i.e., the formation of an image). Imaging technology is the application of materi ...
includes substantial work on the analysis of image data in medical applications. * Finally,
pattern recognition Pattern recognition is the automated recognition of pattern A pattern is a regularity in the world, in human-made design, or in abstract ideas. As such, the elements of a pattern repeat in a predictable manner. A geometric pattern is a kind of ...
is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches and
artificial neural networks Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or node ...
. A significant part of this field is devoted to applying these methods to image data.
Photogrammetry Photogrammetry is the science and technology of obtaining reliable information about physical objects and the environment through the process of recording, measuring and interpreting photographic images and patterns of electromagnetic radiant imag ...
also overlaps with computer vision, e.g., stereophotogrammetry vs.
computer stereo vision Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera imaging A charge-coupled device (CCD) is an integrated circuit An integrated circuit or monolithic integrated circuit ...
.


Applications

Applications range from tasks such as industrial
machine vision 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 ma ...

machine vision
systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer-vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for: * Automatic inspection, ''e.g.'', in manufacturing applications; * Assisting humans in identification tasks, e.g., a species identification system; * Controlling processes, ''e.g.'', an
industrial robot An industrial robot is a robot A robot is a machine—especially one Computer program, programmable by a computer—capable of carrying out a complex series of actions automatically. A robot can be guided by an external control device, o ...
; * Detecting events, ''e.g.'', for visual surveillance or
people countingA people counter is an electronic device that is used to measure the number of people traversing a certain passage or entrance. Examples include simple manual clickers, smart-flooring technologies, infrared beams, thermal imaging systems, WiFi tracke ...
, e.g., in the restaurant industry; * Interaction, ''e.g.'', as the input to a device for computer-human interaction; * Modeling objects or environments, ''e.g.'', medical image analysis or topographical modeling; * Navigation, ''e.g.'', by an
autonomous vehicle A self-driving car, also known as an autonomous vehicle (AV or auto), driverless car, or robo-car is a vehicle that is capable of sensing its environment and moving safely with little or no human input. Self-driving cars combine a variety o ...
or
mobile robot A mobile robot, is a robot that is capable of moving in the surrounding (locomotion).Hu, J.; Bhowmick, P.; Lanzon, A.,Group Coordinated Control of Networked Mobile Robots with Applications to Object Transportation IEEE Transactions on Vehicular Tec ...
; and * Organizing information, ''e.g.'', for indexing databases of images and image sequences. *Tracking surfaces or planes in 3D coordinates for allowing Augmented Reality experiences.


Medicine

One of the most prominent application fields is medical computer vision, or medical image processing, characterized by the extraction of information from image data to diagnose a patient. An example of this is detection of
tumour A neoplasm () is a type of abnormal and excessive growth of . The process that occurs to form or produce a neoplasm is called neoplasia. The growth of a neoplasm is uncoordinated with that of the normal surrounding tissue, and persists in grow ...
s,
arteriosclerosis Arteriosclerosis is the thickening, hardening, and loss of elasticity of the walls of arteries. This process gradually restricts the blood flow to one's organs and tissues and can lead to severe health risks brought on by atherosclerosis Athero ...
or other malign changes; measurements of organ dimensions, blood flow, etc. are another example. It also supports medical research by providing new information: ''e.g.'', about the structure of the brain, or about the quality of medical treatments. Applications of computer vision in the medical area also includes enhancement of images interpreted by humans—ultrasonic images or X-ray images for example—to reduce the influence of noise.


Machine Vision

A second application area in computer vision is in industry, sometimes called
machine vision 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 ma ...

machine vision
, where information is extracted for the purpose of supporting a production process. One example is quality control where details or final products are being automatically inspected in order to find defects. Another example is measurement of position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in agricultural process to remove undesirable food stuff from bulk material, a process called
optical sorting Optical sorting (sometimes called digital sorting) is the automated process of sorting solid products using cameras A camera is an optical instrument used to capture an image An SAR radar imaging, radar image acquired by the SIR-C/X-S ...
.


Military

Military applications are probably one of the largest areas for computer vision. The obvious examples are detection of enemy soldiers or vehicles and
missile guidance Missile guidance refers to a variety of methods of guiding a missile or a guided bomb to its intended target. The missile's target accuracy is a critical factor for its effectiveness. Guidance systems improve missile accuracy by improving its Pr ...
. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors, including image sensors, provide a rich set of information about a combat scene which can be used to support strategic decisions. In this case, automatic processing of the data is used to reduce complexity and to fuse information from multiple sensors to increase reliability.


Autonomous vehicles

One of the newer application areas is autonomous vehicles, which include
submersible A submersible is a small watercraft designed to operate underwater. The term ''submersible'' is often used to differentiate from other underwater vessels known as submarines, in that a submarine is a fully autonomous craft, capable of renewi ...

submersible
s, land-based vehicles (small robots with wheels, cars or trucks), aerial vehicles, and unmanned aerial vehicles (
UAV , a hunter-killer surveillance UAV , rotorcraft A rotorcraft or rotary-wing aircraft is a heavier-than-air aircraft An aircraft is a vehicle that is able to flight, fly by gaining support from the Atmosphere of Earth, air. It counters th ...

UAV
). The level of autonomy ranges from fully autonomous (unmanned) vehicles to vehicles where computer-vision-based systems support a driver or a pilot in various situations. Fully autonomous vehicles typically use computer vision for navigation, ''e.g.'' for knowing where it is, or for producing a map of its environment ( SLAM) and for detecting obstacles. It can also be used for detecting certain task-specific events, ''e.g.'', a UAV looking for forest fires. Examples of supporting systems are obstacle warning systems in cars and systems for autonomous landing of aircraft. Several car manufacturers have demonstrated systems for autonomous driving of cars, but this technology has still not reached a level where it can be put on the market. There are ample examples of military autonomous vehicles ranging from advanced missiles to UAVs for recon missions or missile guidance. Space exploration is already being made with autonomous vehicles using computer vision, ''e.g.'',
NASA The National Aeronautics and Space Administration (NASA; ) is an independent agencies of the United States government, independent agency of the Federal government of the United States, U.S. federal government responsible for the civilian Li ...

NASA
's ''
Curiosity Curiosity (from Latin ''wikt:curiositas#Latin, cūriōsitās'', from ''cūriōsus'' "careful, diligent, curious", akin to ''cura'' "care") is a quality related to inquisitive thinking such as exploration, investigation, and learning, evident by ...
'' and
CNSA China National Space Administration (CNSA; ) is the national space agency This is a list of government agencies engaged in activities related to outer space Outer space is the expanse that exists beyond Earth and between astrono ...
's ''
Yutu-2 ''Yutu-2'' is a robotic lunar rover that formed part of the Chinese Chang'e 4 mission to the Moon The Moon is Earth's only proper natural satellite. At one-quarter the diameter of Earth (comparable to the width of Australia ...

Yutu-2
'' rover.


Tactile Feedback

Materials such as rubber and silicon are being used to create sensors that allow for applications such as detecting micro undulations and calibrating robotic hands. Rubber can be used in order to create a mold that can be placed over a finger, inside of this mold would be multiple strain gauges. The finger mold and sensors could then be placed on top of a small sheet of rubber containing an array of rubber pins. A user can then wear the finger mold and trace a surface. A computer can then read the data from the strain gauges and measure if one or more of the pins is being pushed upward. If a pin is being pushed upward then the computer can recognize this as an imperfection in the surface. This sort of technology is useful in order to receive accurate data of the imperfections on a very large surface. Another variation of this finger mold sensor are sensors that contain a camera suspended in silicon. The silicon forms a dome around the outside of the camera and embedded in the silicon are point markers that are equally spaced. These cameras can then be placed on devices such as robotic hands in order to allow the computer to receive highly accurate tactile data. Other application areas include: * Support of
visual effects Visual effects (sometimes abbreviated VFX) is the process by which imagery is created or manipulated outside the context of a live-action shot in filmmaking Filmmaking (film production) is the process by which a motion picture A fil ...
creation for cinema and broadcast, ''e.g.'', camera tracking (matchmoving). *
Surveillance Surveillance is the monitoring of behavior, many activities, or information for the purpose of information gathering, influencing, or directing. This can include observation from a distance by means of electronic equipment, such as (CCTV), ...

Surveillance
. * Driver drowsiness detection * Tracking and counting organisms in the biological sciences


Typical tasks

Each of the application areas described above employ a range of computer vision tasks; more or less well-defined measurement problems or processing problems, which can be solved using a variety of methods. Some examples of typical computer vision tasks are presented below. Computer vision tasks include methods for acquiring, processing,
analyzing Analysis is the process of breaking a complexity, complex topic or Substance theory, substance into smaller parts in order to gain a better understanding of it. The technique has been applied in the study of mathematics and logic since before Ari ...
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 (the input of the retina) into descriptions of the world that can interface with other thought processes and 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.


Recognition

The classical problem in computer vision, image processing, and
machine vision 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 ma ...

machine vision
is that of determining whether or not the image data contains some specific object, feature, or activity. Different varieties of the recognition problem are described in the literature. *
Object recognition The following outline is provided as an overview of and topical guide to object recognition: Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recogn ...
(also called object classification)one or several pre-specified or learned objects or object classes can be recognized, usually together with their 2D positions in the image or 3D poses in the scene. Blippar, Google Goggles and LikeThat provide stand-alone programs that illustrate this functionality. * Identificationan individual instance of an object is recognized. Examples include identification of a specific person's face or fingerprint, handwriting recognition, identification of handwritten digits, or identification of a specific vehicle. * Object detection, Detectionthe image data are scanned for a specific condition. Examples include detection of possible abnormal cells or tissues in medical images or detection of a vehicle in an automatic road toll system. Detection based on relatively simple and fast computations is sometimes used for finding smaller regions of interesting image data which can be further analyzed by more computationally demanding techniques to produce a correct interpretation. Currently, the best algorithms for such tasks are based on convolutional neural networks. An illustration of their capabilities is given by the ImageNet#ImageNet Challenge, ImageNet Large Scale Visual Recognition Challenge; this is a benchmark in object classification and detection, with millions of images and 1000 object classes used in the competition. Performance of convolutional neural networks on the ImageNet tests is now close to that of humans. The best algorithms still struggle with objects that are small or thin, such as a small ant on a stem of a flower or a person holding a quill in their hand. They also have trouble with images that have been distorted with filters (an increasingly common phenomenon with modern digital cameras). By contrast, those kinds of images rarely trouble humans. Humans, however, tend to have trouble with other issues. For example, they are not good at classifying objects into fine-grained classes, such as the particular breed of dog or species of bird, whereas convolutional neural networks handle this with ease. Several specialized tasks based on recognition exist, such as: * Content-based image retrievalfinding all images in a larger set of images which have a specific content. The content can be specified in different ways, for example in terms of similarity relative a target image (give me all images similar to image X) by utilizing reverse image search techniques, or in terms of high-level search criteria given as text input (give me all images which contain many houses, are taken during winter, and have no cars in them). * Pose (computer vision), Pose estimationestimating the position or orientation of a specific object relative to the camera. An example application for this technique would be assisting a robot arm in retrieving objects from a conveyor belt in an assembly line situation or picking parts from a bin. * Optical character recognition (OCR)identifying Character (computing), characters in images of printed or handwritten text, usually with a view to encoding the text in a format more amenable to editing or Search index, indexing (''e.g.'' ASCII). * 2D code readingreading of 2D codes such as Data Matrix, data matrix and QR code, QR codes. * Facial recognition system, Facial recognition * Pattern recognition, Shape Recognition Technology (SRT) in people counter systems differentiating human beings (head and shoulder patterns) from objects


Motion analysis

Several tasks relate to motion estimation where an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene, or even of the camera that produces the images. Examples of such tasks are: * Egomotiondetermining the 3D rigid motion (rotation and translation) of the camera from an image sequence produced by the camera. * video tracking, Trackingfollowing the movements of a (usually) smaller set of interest points or objects (''e.g.'', vehicles, objects, humans or other organisms) in the image sequence. This has vast industry applications as most of high running machineries can be monitored in this way. * Optical flowto determine, for each point in the image, how that point is moving relative to the image plane, ''i.e.'', its apparent motion. This motion is a result both of how the corresponding 3D point is moving in the scene and how the camera is moving relative to the scene.


Scene reconstruction

Given one or (typically) more images of a scene, or a video, scene reconstruction aims at 3D reconstruction, computing a 3D model of the scene. In the simplest case the model can be a set of 3D points. More sophisticated methods produce a complete 3D surface model. The advent of 3D imaging not requiring motion or scanning, and related processing algorithms is enabling rapid advances in this field. Grid-based 3D sensing can be used to acquire 3D images from multiple angles. Algorithms are now available to stitch multiple 3D images together into point clouds and 3D models.


Image restoration

The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More sophisticated methods assume a model of how the local image structures look, to distinguish them from noise. By first analysing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches. An example in this field is inpainting.


System methods

The organization of a computer vision system is highly application-dependent. Some systems are stand-alone applications that solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc. The specific implementation of a computer vision system also depends on whether its functionality is pre-specified or if some part of it can be learned or modified during operation. Many functions are unique to the application. There are, however, typical functions that are found in many computer vision systems. * Image acquisition – A digital image is produced by one or several image sensors, which, besides various types of light-sensitive cameras, include rangefinder, range sensors, tomography devices, radar, ultra-sonic cameras, etc. Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence. The pixel values typically correspond to light intensity in one or several spectral bands (gray images or colour images), but can also be related to various physical measures, such as depth, absorption or reflectance of sonic or electromagnetic waves, or Magnetic resonance imaging, nuclear magnetic resonance. * Pre-processing – Before a computer vision method can be applied to image data in order to extract some specific piece of information, it is usually necessary to process the data in order to assure that it satisfies certain assumptions implied by the method. Examples are: ** Re-sampling to assure that the image coordinate system is correct. ** Noise reduction to assure that sensor noise does not introduce false information. ** Contrast enhancement to assure that relevant information can be detected. ** Scale space representation to enhance image structures at locally appropriate scales. * Feature detection (computer vision), Feature extraction – Image features at various levels of complexity are extracted from the image data. Typical examples of such features are: ** Lines, edge detection, edges and ridge detection, ridges. ** Localized interest point detection, interest points such as corner detection, corners, blob detection, blobs or points. :More complex features may be related to texture, shape or motion. * Object detection, Detection/Image segmentation, segmentation – At some point in the processing a decision is made about which image points or regions of the image are relevant for further processing. Examples are: ** Selection of a specific set of interest points. ** Segmentation of one or multiple image regions that contain a specific object of interest. ** Segmentation of image into nested scene architecture comprising foreground, object groups, single objects or Salience (neuroscience), salient object parts (also referred to as spatial-taxon scene hierarchy), while the Salience (neuroscience), visual salience is often implemented as Visual spatial attention, spatial and Visual temporal attention, temporal attention. ** Segmentation or Object co-segmentation, co-segmentation of one or multiple videos into a series of per-frame foreground masks, while maintaining its temporal semantic continuity. * High-level processing – At this step the input is typically a small set of data, for example a set of points or an image region which is assumed to contain a specific object. The remaining processing deals with, for example: ** Verification that the data satisfy model-based and application-specific assumptions. ** Estimation of application-specific parameters, such as object pose or object size. ** Image recognition – classifying a detected object into different categories. ** Image registration – comparing and combining two different views of the same object. * Decision making Making the final decision required for the application, for example: ** Pass/fail on automatic inspection applications. ** Match/no-match in recognition applications. ** Flag for further human review in medical, military, security and recognition applications.


Image-understanding systems

Image-understanding systems (IUS) include three levels of abstraction as follows: low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events. Many of these requirements are entirely topics for further research. The representational requirements in the designing of IUS for these levels are: representation of prototypical concepts, concept organization, spatial knowledge, temporal knowledge, scaling, and description by comparison and differentiation. While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing. Inference and control requirements for IUS are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction.


Hardware

There are many kinds of computer vision systems; however, all of them contain these basic elements: a power source, at least one image acquisition device (camera, ccd, etc.), a processor, and control and communication cables or some kind of wireless interconnection mechanism. In addition, a practical vision system contains software, as well as a display in order to monitor the system. Vision systems for inner spaces, as most industrial ones, contain an illumination system and may be placed in a controlled environment. Furthermore, a completed system includes many accessories such as camera supports, cables and connectors. Most computer vision systems use visible-light cameras passively viewing a scene at frame rates of at most 60 frames per second (usually far slower). A few computer vision systems use image-acquisition hardware with active illumination or something other than visible light or both, such as structured-light 3D scanners, thermographic cameras, hyperspectral imagers, radar imaging, lidar scanners, magnetic resonance images, side-scan sonar, synthetic aperture sonar, etc. Such hardware captures "images" that are then processed often using the same computer vision algorithms used to process visible-light images. While traditional broadcast and consumer video systems operate at a rate of 30 frames per second, advances in digital signal processing and Graphics processing unit, consumer graphics hardware has made high-speed image acquisition, processing, and display possible for real-time systems on the order of hundreds to thousands of frames per second. For applications in robotics, fast, real-time video systems are critically important and often can simplify the processing needed for certain algorithms. When combined with a high-speed projector, fast image acquisition allows 3D measurement and feature tracking to be realised. Egocentric vision systems are composed of a wearable camera that automatically take pictures from a first-person perspective. As of 2016, vision processing units are emerging as a new class of processor, to complement CPUs and graphics processing units (GPUs) in this role.


See also

* Computational imaging * Computational photography * Computer audition * Egocentric vision * Machine vision glossary * Space mapping * Teknomo–Fernandez algorithm * Vision science * Visual agnosia * Visual perception * Visual system


Lists

* List of computer vision topics * List of emerging technologies * Outline of artificial intelligence * Outline of computer vision


References


Further reading

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External links


USC Iris computer vision conference list


A complete list of papers of the most relevant computer vision conferences.
Computer Vision Online
News, source code, datasets and job offers related to computer vision.


CVonline
Bob Fisher's Compendium of Computer Vision.
British Machine Vision Association
Supporting computer vision research within the UK via the British Machine Vision Conference, BMVC and MIUA conferences, ''Annals of the BMVA'' (open-source journal), BMVA Summer School and one-day meetings
Computer Vision Container, Joe Hoeller GitHub:
Widely adopted open-source container for GPU accelerated computer vision applications. Used by researchers, universities, private companies as well as the U.S. Gov't. {{Authority control Computer vision, Artificial intelligence Image processing Packaging machinery Articles containing video clips