Volume 03 Issue 10-2023
293
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
03
ISSUE
10
Pages:
293-299
SJIF
I
MPACT
FACTOR
(2021:
5.478
)
(2022:
5.636
)
(2023:
6.741
)
OCLC
–
1368736135
A
BSTRACT
This paper examines the areas of application of digital technologies in applied television systems and
modern means of implementing such systems. The results of the use of digital television technologies are
also presented.
K
EYWORDS
software, SaaS, Google Earth, Google Maps.
I
NTRODUCTION
Applied TV with the development of digital
technologies has received processors with neural
architecture, machine learning principles and
Internet services. It has become difficult to draw
a line between the concepts of applied television
systems and computer vision due to the tendency
to integrate previously separate branches of
production [1-3].
In general, short circuit systems, like applied TV
systems, consist of a photo or video camera, as
well as a computer on which image processing
and analysis programs run.
If software security image processing is located
directly in the camera, such a camera is called a
“smart camera”. The sof
tware (software) can also
run on a remote computer or computers, or run in
the cloud using the SaaS model (Software as a
Service) [4-7].
Journal
Website:
http://sciencebring.co
m/index.php/ijasr
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Research Article
EQUIPMENT AND TECHNOLOGIES USED TO IMPLEMENT
APPLIED TV TASKS AT THE PRESENT STAGE OF
DEVELOPMENT
Submission Date:
October 20, 2023,
Accepted Date:
October 25, 2023,
Published Date:
October 30, 2023
Crossref doi:
https://doi.org/10.37547/ijasr-03-10-45
I. Makhmudov
Teacher, Department Of Telecommunication Engineering, Faculty Of Telecommunication Technologies And
Professional Education, Fergana Branch Of Tuit, Fergana, Uzbekistan
Volume 03 Issue 10-2023
294
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
03
ISSUE
10
Pages:
293-299
SJIF
I
MPACT
FACTOR
(2021:
5.478
)
(2022:
5.636
)
(2023:
6.741
)
OCLC
–
1368736135
Fig. 1. Structure of a computer vision system with a Smart camera
Computer vision systems include the following
main components (Fig. 1):
object illumination (not always required)
and optics (lenses and objectives);
sensor matrix for image projection;
systems for processing images obtained
from the matrix.
In necessary cases, such as indoors, where the
light can be controlled, the part of the object that
needs to be inspected can be illuminated so that
the desired characteristics of the object are
visible to the camera.
The optical system projects the resulting image in
the form of a spectrum visible or invisible to the
human eye onto the sensor matrix. The camera's
sensor matrix converts the image into a digital
image, which is then sent to the processor for
analysis [8-12].
In most cases, short circuit systems are designed
to operate in natural light. In addition, short-
circuit systems can operate in ranges invisible to
the human eye [13-19].
To work in low-light conditions, cameras with
illumination can be used, in which a ring light
source provides bright, uniform illumination of
the object when it is necessary to highlight the
texture of the material, small details, etc. Lighting
also helps to get rid of glare, illumination of the
object, and is used in difficult conditions, for
example, in the fog.
Pixel density (sensor resolution) is very
important for the correct operation of a computer
vision application. The higher the resolution, the
more detail there will be in the image, the more
accurate the measurements will be. The required
pixel density depends on the size of the object, the
working distance of the camera and other
parameters.
Types of computer vision systems used in applied
TV
There are three main types of short circuit
systems:
one-dimensional (1D),
two-dimensional (2D),
volumetric (3D) systems.
Based on the type of lenses in the lens and the
number of cameras, a distinction is made between
panoramic multi-camera systems and fisheye
systems [20-24].
Stereo vision is one of the methods for extracting
information about the depth of a scene using
images from two cameras (stereo pair). The
method is based on the principle of human vision,
when the human brain receives information
Volume 03 Issue 10-2023
295
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
03
ISSUE
10
Pages:
293-299
SJIF
I
MPACT
FACTOR
(2021:
5.478
)
(2022:
5.636
)
(2023:
6.741
)
OCLC
–
1368736135
about the volume of a picture from two eyes.
Similarly, the difference in the arrangement of
pixels in the image from the two cameras
provides information about depth (Fig. 2.).
Fig. 2. Principle of stereo vision
By adjusting the distance between the cameras of
a stereo pair (baseline), you can adjust the
required depth of scene recognition.
Spherical (panoramic) fisheye systems are used
to emulate panoramic cameras for video
surveillance and to integrate broadcast webcams
into 2D and 3D geographic information system
(GIS) applications such as Google Earth and
GoogleMaps [8].
Panoramic fisheye systems working with image
processing applications of cloud providers are
used, for example, in driver assistance systems
(ADAS), self-driving cars, when monitoring large
areas and counting the number of people (Fig. 3.)
Fig. 3. Fisheye camera image
Arrays (networks) of cameras are used to track
the movement of individuals indoors or in places
with limited visibility (warehouses in seaports,
factory areas, etc.), as well as for traffic control in
intelligent transport systems (ITS).
Systems of small number (2
–
6) cameras are used
for areas such as:
Automation of production,
Video surveillance from an unmanned
aerial vehicle UAV,
Volume 03 Issue 10-2023
296
International Journal of Advance Scientific Research
(ISSN
–
2750-1396)
VOLUME
03
ISSUE
10
Pages:
293-299
SJIF
I
MPACT
FACTOR
(2021:
5.478
)
(2022:
5.636
)
(2023:
6.741
)
OCLC
–
1368736135
Face recognition, motion recognition,
identification, etc.
For example, the use of a multi-chamber system
of five chambers on a conveyor belt during mass
production greatly facilitates product quality
control (Fig. 4.).
Fig. 4. System of 5 cameras for quality control of products on the conveyor
Methods of functioning of computer vision
systems
Computer vision software libraries used to
implement tasks in various programming
languages and interactive environments:
Open CV (Open Source Computer Vision Library)
is a library of computer vision algorithms, image
processing and general-purpose numerical
algorithms. Implemented in C/ C++, also
developed for Python, Java, Ruby, Mat lab, Lua
and other languages.
PCL (Point Cloud Library) is a large-scale open
source project for processing 2D/3D images and
point clouds. The PCL platform contains a variety
of algorithms, including filtering, feature
estimation, surface reconstruction, registration,
model fitting, and segmentation.
ROS (Robot Operating System)
–
software
development platform for robots. It is a set of
tools, libraries, and conventions that make it easy
to develop complex and efficient programs to
control many types of robots.
MATLAB is a high-level language and interactive
environment for programming, numerical
calculations and visualization of results. Using
MATLAB, you can analyze data, develop
algorithms, and create models and applications.
Nvidia GPUs.
These packages contain built-in functions for
implementing basic approaches to solving
problems assigned to this applied TV system.
Among them:
−
Contour analysis;
−
Search By template matching;
Volume 03 Issue 10-2023
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International Journal of Advance Scientific Research
(ISSN
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2750-1396)
VOLUME
03
ISSUE
10
Pages:
293-299
SJIF
I
MPACT
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(2021:
5.478
)
(2022:
5.636
)
(2023:
6.741
)
OCLC
–
1368736135
−
Search outside patterns, matching By key
points
(feature
detection,
description
matching);
−
Combination of data (Data Fusion).
Computer vision is not limited only to these basic
methods; for example, we can distinguish so-
called genetic algorithms, used, in particular, for
face recognition.
When contour analysis of an image from a video
sequence, not the full image of an object is
analyzed, but only its contour, which significantly
reduces the complexity of algorithms and
calculations during processing. An object outline
is a curve that corresponds to the boundary of an
object in an image. The limitations of the contour
analysis method include:
•
with the same brightness as the background,
the object may not have a clear boundary in
the image or it may be “noisy” with
interference, which makes it impossible to
isolate the contour;
•
overlapping objects or their grouping leads to
the fact that the outline is highlighted
incorrectly and does not correspond to the
boundary of the object;
•
poor resistance to interference, leading to the
fact that any violation of the integrity of the
circuit or poor visibility of the object leads to
either the impossibility of detection or false
alarms.
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