ARTIFICIAL
VISION
Computer
vision is the science and technology of machines that see. As a scientific
discipline, computer vision is concerned with the theory 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:
- 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.
ARTIFICIAL
VISION SYSTEM
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
Lines, edges and ridges.
Localized interest points such as corners, blobs or points.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.
Information by Wikipedia