Computer Vision - Important Concepts in Signal Processing, Image Processing and Data Compression - page 13

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.

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. An example in this field is the inpainting.

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 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 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 sensors,
which, besides various types of light-sensitive cameras, include 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.

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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
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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.

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Contrast enhancement to assure that relevant information can be detected.

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Scale-space representation to enhance image structures at locally
appropriate scales.

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Feature extraction : Image features at various levels of complexity are extracted
from the image data. Typical examples of such features are

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