|
Computer vision is the science and technology of machines that see.
As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multi-dimensional data from a medical scanner.
As a technological discipline, computer vision seeks to apply the theories and models of computer vision to the construction of computer vision systems. Examples of applications of computer vision systems include systems for The scientific study of the theories and techniques for building systems that perform perception using images or multi-dimensional data. ...
- Controlling processes (e.g. an industrial robot or an autonomous vehicle).
- Detecting events (e.g. for visual surveillance or people counting)
- Organizing information (e.g. for indexing databases of images and image sequences),
- Modeling objects or environments (e.g. industrial inspection, medical image analysis or topographical modeling),
- Interaction (e.g. as the input to a device for computer-human interaction).
Computer vision can also be described as a complement (but not necessarily the opposite) of biological vision. In biological vision, the visual perception of humans and various animals are studied, resulting in models of how these systems operate in terms of physiological processes. Computer vision, on the other hand, studies and describes artificial vision system that are implemented in software and/or hardware. Interdisciplinary exchange between biological and computer vision has proven increasingly fruitful for both fields. An industrial robot is officially defined by ISO (Standard 8373:1994, Manipulating Industrial Robots – Vocabulary) as an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes. ...
Autonomous robots are robots which can perform desired tasks in unstructured environments without continuous human guidance. ...
A people counter is a device used to measure the number and direction of people traversing a certain passage or entrance per time unit. ...
Human-computer interaction (HCI) is the study of interaction between people (users) and computers. ...
Sub-domains of computer vision include scene reconstruction, event detection, tracking, object recognition, learning, indexing, ego-motion and image restoration.
State of the art
 Relation between Computer vision and various other fields The field of computer vision can be characterized as immature and diverse. Even though earlier work exists, it was not until the late 1970s that a more focused study of the field started when computers could manage the processing of large data sets such as images. However, these studies usually originated from various other fields, and consequently there is no standard formulation of "the computer vision problem". Also, and to an even larger extent, there is no standard formulation of how computer vision problems should be solved. Instead, there exists an abundance of methods for solving various well-defined computer vision tasks, where the methods often are very task specific and seldom can be generalized over a wide range of applications. Many of the methods and applications are still in the state of basic research, but more and more methods have found their way into commercial products, where they often constitute a part of a larger system which can solve complex tasks (e.g., in the area of medical images, or quality control and measurements in industrial processes). In most practical computer vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Image File history File links CVoverview2. ...
A significant part of artificial intelligence deals with autonomous planning or deliberation for system which can perform mechanical actions such as moving a robot through some environment. This type of processing typically needs input data provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot. Other parts which sometimes are described as belonging to artificial intelligence and which are used in relation to computer vision is pattern recognition and learning techniques. As a consequence, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general. AI redirects here. ...
Pattern recognition is a field within the area of machine learning. ...
Physics is another field that is strongly related to computer vision. A significant part of computer vision deals with methods which require a thorough understanding of the process in which electromagnetic radiation, typically in the visible or the infra-red range, is reflected by the surfaces of objects and finally is measured by the image sensor to produce the image data. This process is based on optics and solid state physics. More sophisticated image sensors even require quantum mechanics to provide a complete comprehension of the image formation process. Also, various measurement problems in physics can be addressed using computer vision, for example related to motion in fluids. Consequently, computer vision can also be seen as an extension of physics. A magnet levitating above a high-temperature superconductor demonstrates the Meissner effect. ...
A third field which plays an important role is neurobiology, 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 subfield within computer vision where artificial systems are designed to mimic the processing and behaviour of biological systems, at different levels of complexity. Also, some of the learning-based methods developed within computer vision have their background in biology. Neurobiology is the study of cells of the nervous system and the organization of these cells into functional circuits that process information and mediate behavior. ...
Yet another field related to computer vision is 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 which have no counterpart in the processing of one-variable signals. A distinct character of these methods is the fact that they are non-linear which, together with the multi-dimensionality of the signal, defines a subfield in signal processing as a part of computer vision. Signal processing is the processing, amplification and interpretation of signals, and deals with the analysis and manipulation of signals. ...
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, optimization or 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. This article is about the field of statistics. ...
In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set. ...
For other uses, see Geometry (disambiguation). ...
Related fields Computer vision, Image processing, Image analysis, Robot vision and Machine vision are closely related fields. If you look inside text books which have any of these names in the title there is a significant overlap in terms of what techniques and applications they cover. This implies that the basic techniques that are used and developed in these fields are more or less identical, something which can be interpreted as there is only one field with different names. UPIICSA IPN - Binary image Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image. ...
Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. ...
Machine vision (MV) is the application of computer vision to industry and manufacturing. ...
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. The following characterizations appear relevant but should not be taken as universally accepted.
Image processing and Image analysis 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. UPIICSA IPN - Binary image Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image. ...
Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. ...
Computer vision tends to focus on 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 tends to focus on applications, mainly in industry, e.g., vision based autonomous robots and systems for vision based inspection or measurement. 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 emphasized by means of efficient implementations in hardware and software. Machine vision (MV) is the application of computer vision to industry and manufacturing. ...
There is also a field called Imaging which primarily focus on the process of producing images, but sometimes also deals with processing and analysis of images. For example, Medical imaging contains lots of work on the analysis of image data in medical applications. Imaging refers to the science of obtaining pictures or more complicated spatial representations, such as animations or 3-D computer graphics models, from physical things. ...
Medical imaging designates the ensemble of techniques and processes used to create images of the human body (or parts thereof) for clinical purposes (medical procedures seeking to reveal, diagnose or examine disease) or medical science (including the study of normal anatomy and function). ...
Finally, pattern recognition is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches. A significant part of this field is devoted to applying these methods to image data. Pattern recognition is a field within the area of machine learning. ...
A consequence of this state of affairs is that you can be working in a lab related to one of these fields, apply methods from a second field to solve a problem in a third field and present the result at a conference related to a fourth field!
Examples of applications for computer vision One of the most prominent application fields is medical computer vision or medical image processing. This area is characterized by the extraction of information from image data for the purpose of making a medical diagnosis of a patient. Generally, image data is in the form of microscopy images, X-ray images, angiography images, ultrasonic images, and tomography images. An example of information which can be extracted from such image data is detection of tumours, arteriosclerosis or other malign changes. It can also be measurements of organ dimensions, blood flow, etc. This application area also supports medical research by providing new information, e.g., about the structure of the brain, or about the quality of medical treatments. Microscopy is any technique for producing visible images of structures or details too small to otherwise be seen by the human eye, using a microscope or other magnification tool. ...
In the NATO phonetic alphabet, X-ray represents the letter X. An X-ray picture (radiograph) taken by Röntgen An X-ray is a form of electromagnetic radiation with a wavelength approximately in the range of 5 pm to 10 nanometers (corresponding to frequencies in the range 30 PHz...
Angiography or arteriography is a medical imaging technique in which an X-ray picture is taken to visualize the inner opening of blood filled structures, including arteries, veins and the heart chambers. ...
Medical ultrasonography is an ultrasound-based imaging diagnostic technique used to visualize internal organs, their size, structure and their pathological lesions. ...
Tomography is imaging by sections or sectioning. ...
Tumor (American English) or tumour (British English) originally means swelling, and is sometimes still used with that meaning. ...
// Introduction Arteriosclerosis means the hardening of the arteries in Greek. ...
A second application area in computer vision is in industry. Here, information is extracted for the purpose of supporting a manufacturing 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.
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. 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. A guided bomb strikes an underground facility Missile guidance technologies of missile systems use a variety of methods to guide a missile to its intended target. ...
 Artist's Concept of Rover on Mars, an example of an unmanned land-based vehicle. Notice the stereo cameras mounted on top of the Rover. (credit: Maas Digital LLC) One of the newer application areas is autonomous vehicles, which include submersibles, land-based vehicles (small robots with wheels, cars or trucks), aerial vehicles, and unmanned aerial vehicles (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, i.e. 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's Mars Exploration Rover. Image File history File linksMetadata Download high resolution version (3000x2400, 963 KB)This image may be viewed in 3D stereo with same 3D glasses as other NASA Mars images. ...
Image File history File linksMetadata Download high resolution version (3000x2400, 963 KB)This image may be viewed in 3D stereo with same 3D glasses as other NASA Mars images. ...
Stereo cameras is one method of distilling a noisy video signal into a coherent data set that a computer can begin to process into actionable symbolic objects, or abstractions. ...
Unmanned Aerial Vehicle over Iraq. ...
Simultaneous localization and mapping (SLAM) is a technique used by robots and autonomous vehicles to build up a map within an unknown environment while at the same time keeping track of their current position. ...
The driverless car is an emerging family of technologies, ultimately aimed at a full taxi-like experience for car users, but without a driver. ...
Artists Concept of Rover on Mars (credit: Maas Digital LLC) NASAs Mars Exploration Rover (MER) Mission is an ongoing robotic mission of exploring Mars, that began in 2003 with the sending of two rovers — Spirit and Opportunity — to explore the Martian surface and geology. ...
Other application areas include:
Visual effects (or VFX for short) is the term given in which images or film frames are created and manipulated for film and video. ...
In 3d graphic, is the process of finding, given a picture, the camera coordinates and properties that are compatible with those of the virtual camera that took the picture. ...
Typical tasks of computer vision 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.
Recognition The classical problem in computer vision, image processing and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity. This task can normally be solved robustly and without effort by a human, but is still not satisfactorily solved in computer vision for the general case: arbitrary objects in arbitrary situations. The existing methods for dealing with this problem can at best solve it only for specific objects, such as simple geometric objects (e.g., polyhedrons), human faces, printed or hand-written characters, or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and pose of the object relative to the camera. In computer vision, pose refers to the 3D orientation of a specific object (or living being) depicted in an image or set of images. ...
Different varieties of the recognition problem are described in the literature:
- Recognition: 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.
- Identification: An individual instance of an object is recognized. Examples: identification of a specific person's face or fingerprint, or identification of a specific vehicle.
- Detection: the image data is scanned for a specific condition. Examples: 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.
Several specialized tasks based on recognition exist, such as: - Content-based image retrieval: finding 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), or in terms of high-level search criteria given as text input (give me all images which contains many houses, are taken during winter, and have no cars in them).
- Pose estimation: estimating 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.
- Optical character recognition (or OCR): identifying characters in images of printed or handwritten text, usually with a view to encoding the text in a format more amenable to editing or indexing (e.g. ASCII).
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. ...
In computer vision, pose refers to the 3D orientation of a specific object (or living being) depicted in an image or set of images. ...
Modern car assembly line. ...
Optical character recognition, usually abbreviated to OCR, is a type of computer software designed to translate images of handwritten or typewritten text (usually captured by a scanner) into machine-editable text, or to translate pictures of characters into a standard encoding scheme representing them (e. ...
A search index, or more precisely a full-text index, is the database which a full-text search engine uses to respond to the query issued by the user. ...
Image:ASCII fullsvg There are 95 printable ASCII characters, numbered 32 to 126. ...
Motion Several tasks relate to motion estimation, in which an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene. Examples of such tasks are: - Egomotion: determining the 3D rigid motion of the camera.
- Tracking: following the movements of objects (e.g. vehicles or humans).
Video tracking is the process of following movable objects in time using a camera. ...
Scene reconstruction Given one or (typically) more images of a scene, or a video, scene reconstruction aims at 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. A computer simulation or a computer model is a computer program which attempts to simulate an abstract model of a particular system. ...
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 like, a model which distinguishes them from the 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.
Computer vision systems The organization of a computer vision system is highly application dependent. Some systems are stand-alone applications which solve a specific measurement or detection problem, while other 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 if its functionality is pre-specified or if some part of it can be learned or modified during operation. There are, however, typical functions which are found in many computer vision systems. - Image acquisition: A digital image is produced by one or several image sensor which, besides various types of light-sensitive cameras, includes 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 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 in order to assure that the image coordinate system is correct.
- Noise reduction in order 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 extraction: Image features at various levels of complexity are extracted from the image data. Typical examples of such features are
- More complex features may be related to texture, shape or motion.
- Detection/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 which contain a specific object of interest.
- 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.
- Classifying a detected object into different categories.
MRI redirects here. ...
Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities. ...
The goal of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply. ...
In a 2-D function, a (bright) ridge is a connected set of points that are maximal in at least one dimension. ...
Interest point detection is a recent terminology in computer vision that refers to the detection of interest points for subsequent processing. ...
Corner detection or the more general terminology interest point detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. ...
In the area of computer vision, blob detection refers to visual modules that are aimed at detecting points and/or regions in the image that are either brighter or darker than the surrounding. ...
See also AI redirects here. ...
Digital image processing is the use of computer algorithms to perform image processing on digital images. ...
UPIICSA IPN - Binary image Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image. ...
This is a list of Computer Vision and Image Processing topics // Image Enhancement Image denoising Image histogram Histogram equalization Tone mapping Retinex algorithm Gamma correction Transformations Affine transform Projective transform Radon transform Walsh-Hadamard Transform Filtering, Fourier and Wavelet transforms and image compression Filter bank Gabor filter JPEG2000 Adaptive filtering...
As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. ...
Machine vision (MV) is the application of computer vision to industry and manufacturing. ...
Common definitions related to the Machine Vision field. ...
Medical imaging designates the ensemble of techniques and processes used to create images of the human body (or parts thereof) for clinical purposes (medical procedures seeking to reveal, diagnose or examine disease) or medical science (including the study of normal anatomy and function). ...
Pattern recognition is a field within the area of machine learning. ...
Further reading Sorted alphabetically with respect to first author's family name - Wilhelm Burger and Mark J. Burge (2007). Digital Image Processing: An Algorithmic Approach Using Java. Springer. ISBN 1846283795 and ISBN 3540309403.
- J. L. Crowley and H. I. Christensen (Eds.) (1995). Vision as Process. Springer-Verlag. ISBN 3-540-58143-X and ISBN 0-387-58143-X.
- E. R. Davies (2005). Machine Vision : Theory, Algorithms, Practicalities. Morgan Kaufmann. ISBN 0-12-206093-8.
- Olivier Faugeras (1993). Three-Dimensional Computer Vision, A Geometric Viewpoint. MIT Press. ISBN 0-262-06158-9.
- R. Fisher, K Dawson-Howe, A. Fitzgibbon, C. Robertson, E. Trucco (2005). Dictionary of Computer Vision and Image Processing. John Wiley. ISBN 0-470-01526-8.
- David A. Forsyth and Jean Ponce (2003). Computer Vision, A Modern Approach. Prentice Hall. ISBN 0-12-379777-2.
- Gösta H. Granlund and Hans Knutsson (1995). Signal Processing for Computer Vision. Kluwer Academic Publisher. ISBN 0-7923-9530-1.
- Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in computer vision. Cambridge University Press. ISBN 0-521-54051-8.
- Berthold Klaus Paul Horn (1986). Robot Vision. MIT Press. ISBN 0-262-08159-8.
- Bernd Jähne and Horst Haußecker (2000). Computer Vision and Applications, A Guide for Students and Practitioners. Academic Press. ISBN 0-13-085198-1.
- Bernd Jähne (2002). Digital Image Processing. Springer. ISBN 3-540-67754-2.
- Reinhard Klette, Karsten Schluens and Andreas Koschan (1998). Computer Vision - Three-Dimensional Data from Images. Springer, Singapore. ISBN 981-3083-71-9.
- Tony Lindeberg (1994). Scale-Space Theory in Computer Vision. Springer. ISBN 0-7923-9418-6.
- David Marr (1982). Vision. W. H. Freeman and Company. ISBN 0-7167-1284-9.
- Gérard Medioni and Sing Bing Kang (2004). Emerging Topics in Computer Vision. Prentice Hall. ISBN 0-13-101366-1.
- Tim Morris (2004). Computer Vision and Image Processing. Palgrave Macmillan. ISBN 0-333-99451-5.
- Nikos Paragios and Yunmei Chen and Olivier Faugeras (2005). Handbook of Mathematical Models in Computer Vision. Springer. ISBN 0-387-26371-3.
- Azriel Rosenfeld and Avinash Kak (1982). Digital Picture Processing. Academic Press. ISBN 0-12-597301-2.
- Linda G. Shapiro and George C. Stockman (2001). Computer Vision. Prentice Hall. ISBN 0-13-030796-3.
- Milan Sonka, Vaclav Hlavac and Roger Boyle (1999). Image Processing, Analysis, and Machine Vision. PWS Publishing. ISBN 0-534-95393-X.
- Emanuele Trucco and Alessandro Verri (1998). Introductory Techniques for 3-D Computer Vision. Prentice Hall. ISBN 0132611082.
Springer is the name of several places in the United States: Springer, New Mexico Springer Township, North Dakota Springer, Oklahoma Springer is the name of: Springer Science+Business Media, a worldwide publishing group based in Germany (including Springer-Verlag) Axel Springer Verlag AG, famous conservative German publishing house Springer (EP...
External links
General resources Wikia (no official pronunciation[2]; originally Wikicities) is a selective wiki hosting service (or wiki farm) operated by Wikia, Inc. ...
Look up Wiki in Wiktionary, the free dictionary. ...
Tutorials - CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision
- Computer Vision Blog (Spanish)
- Starting with Computer Vision using Visual Basic (Spanish)
Papers - Machine Perception of Three-Dimensional Solids - the paper mentioned by Joseph Mundy in the video
|
Feeds |
Contact