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Rendering In 3D
State of the art
Computer vision is a diverse and relatively new field of
study. In the early days of computing, it was difficult
to process even moderately large sets of image data. It
was not until the late 1970s that a more focused study
of the field emerged. Computer vision covers a wide
range of topics which are often related to other
disciplines, and consequently there is no standard
formulation of "the computer vision problem". Moreover,
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 generalised 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.
Much of artificial intelligence deals with autonomous
planning or deliberation for robotical 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. Artificial
intelligence and computer vision share other topics such
as pattern recognition and learning techniques.
Consequently, computer vision is sometimes seen as a
part of the artificial intelligence field or the
computer science field in general.
Physics is another field that is closely related to
computer vision. Computer vision systems rely on image
sensors which detect electromagnetic radiation which is
typically in the form of either visible or infra-red
light. The sensors are designed using solid-state
physics. The process by which light propagates and
reflects off surfaces is explained using optics.
Sophisticated image sensors even require quantum
mechanics 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.
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 behavior of
biological systems, at different levels of complexity.
Also, some of the learning-based methods developed
within computer vision have their background in biology.
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.
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 realised 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.
The fields most closely related to computer vision are
image processing, image analysis and 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 more or less identical,
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.
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 characterisation implies that image
processing/analysis neither require assumptions nor
produce interpretations about the image content.
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
manufacturing, 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
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 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.
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.
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.
A second application area in computer vision is in
industry, sometimes called machine vision, where
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. Machine vision is also heavily
used in agricultural process to remove undesirable food
stuff from bulk material, a process called optical
sorting.
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.
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.
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
and ESA's ExoMars Rover.
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