=Paper=
{{Paper
|id=Vol-3421/short4
|storemode=property
|title=Method for Obtaining Shadow Images of Control Objects for Telemetric Care Systems (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3421/short4.pdf
|volume=Vol-3421
|authors=Lidiia Tereshchenko
|dblpUrl=https://dblp.org/rec/conf/cpits/Tereshchenko23
}}
==Method for Obtaining Shadow Images of Control Objects for Telemetric Care Systems (short paper)==
https://ceur-ws.org/Vol-3421/short4.pdf
Method for Obtaining Shadow Images of Control Objects
for Telemetric Care Systems
Lidiia Tereshchenko1
1
National Aviation University, 1 Lubomyr Huzar ave., Kyiv, 03058, Ukraine
Abstract
The paper deals with two-dimensional spectral detectors for baggage inspection X-ray
devices. This detector is based on the construction of analytical models for the internal
structure of objects under control and their spectrum calculation. The methods of projective
geometry and Bouguer-Lambert law are applied to obtain the analytical models for shadows
of three-dimensional objects. Spectral detectors are designed according to the Neyman-
Pearson criterion. Analysis shows that the proposed spectral detector has good operating
characteristics even at low signal-to-noise ratios.
Keywords 1
Keywords: aviation security service, X-ray, optical imaging, shadow of three-dimensional
objects, spectral detector.
1. Introduction 2. Problem Statement
Ensuring effective protection against terrorism The paper addresses applied research
is the most difficult issue, especially for countries challenges concerning the development and
with a developed air transport network, a large application of a new method of determination
number of airlines, and airports. The problem is (visualization) of the internal structure of the
complicated by the unpredictability of terrorists’ Objects Under Control (OC), that enables
actions. In addition, vulnerabilities in aviation dangerous OC to be identified with high
security systems (such as procedures for screening probability in real-time, increases the speed of
airline passengers and their baggage, freight dangerous substances identification in luggage,
shipments, mail, etc.) that can be exploited by law and provides automation of these processes. In
violators should be taken into consideration. The addition, the automatic generation of images of
main way to improve aviation safety is to prevent hazardous OC allows for periodic inspections of
hazardous objects and substances, explosive aviation security service operators. Detection
devices, and weapons on aircraft boards. This systems based on X-ray, computer tomography,
requires a comprehensive development and and spectroscopy of mobile ions have certain
introduction of new methods of screening, shortcomings [3–9]. Some of these systems can
detection, and identification of dangerous objects detect well-hidden explosives, but their
under control. Insights of the direct visualization implementation requires considerable funds. In
methods indicate that they are inherent in the same addition, they have a high level of false alarms
type of operations: primary radiation exposure of (approximately 0.2 ... 0.4). Thus, the development
the objects under control in configuration space of analytical models for the receipt of
(in the case of active method), reradiation multidimensional shadows of translucent objects
reception (scattered or passed through the object), for further processing will allow the classification
its conversion into an electrical signal, signal of OC, which will greatly facilitate the work of
processing and electrical-to-optical signal operators serving supervision devices in Aviation
conversion [1, 2]. Security Service (AvSS), reducing the value of
false alarms. Literature analysis showed that the
CPITS 2023: Workshop on Cybersecurity Providing in Information and Telecommunication Systems, February 28, 2023, Kyiv, Ukraine
EMAIL: 10118@ukr.net (L. Tereshchenko);
ORCID: 0000-0001-8183-9016 (L. Tereshchenko);
©️ 2023 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
191
modernization of equipment for AvSS is carried irradiation by a point source located on the axis of
out in two directions: in the part of the object symmetry perpendicular to the plane of the
improvement of hardware and software. In [10] image (screen). To determine the position of the
authors proposed a new X-ray backscatter radiation source, the OC, and the screen with a
technique using an un-collimated powerful (high point source it is appropriate to use the cylindrical
kW) X-ray beam and an efficient pinhole camera coordinate system applied to the Fig. 1. The OC
encompassed with a high-resolution matrix model with complex form is presented in Fig. 2.
detector for imaging of an object. Moreover, a
high-energy X-ray inspection technique for the
reliable inspection of air freight containers was
presented in [11]. Analysis of various strategies
for object detection in X-ray security imagery is
given in [12]. Moreover, the paper [13] also deals
with a technique for the classification of X-ray
baggage images using convolutional neural
networks. The application of deep convolutional
neural network as a classification method in
Figure 1: OC scanning: (а) is the setting of a
medicine X-ray image analysis was considered in
[14]. cylindrical coordinate system; (b) is the setting of
In [15] authors investigated the feasibility a scanning beam position
of applying straight-line-trajectory-based
tomographic imaging configurations to security
inspections. The method of automated target
recognition with the usage of a reference database,
which contains X-ray images of OC, for cargo
scanning systems was proposed in [16]. The
papers [17, 18] deal with procedures of handguns,
shuriken, and razor blades recognition for
baggage inspection. The simulation of the internal
structure for OC with simple and complex forms
using the point source of irradiation in the center,
as well as with the bias relative to the center, is
considered in [19]. The method developed for
optical imaging of the inner structure of the three-
dimensional objects allows obtaining a shadow of Figure 2: OC with complex form
these objects, exposed to electromagnetic
radiation. It has useful applications in different Internal visualization of the OC with a
life spheres, such as in medicine, the complex form, in this case, a sphere in the sphere,
manufacturing industry, the process of customs designed with a point source is shown in Fig. 3.
supervision of goods and means of transport for
commercial use, etc. It allows the AvSS to
increase the probability of correct detection of
hazardous materials and reduce false alarms in its
security system. For medicine, the method may
help to increase the probability of health hazard
anomaly detection. So aim of this paper is a
synthesis of a two-dimensional spectral detector
for baggage inspection X-ray devices.
3. Materials and Methods
The construction of an analytical model
reduces to the calculation of a projective image of
an isotropic object in the case of homogeneous Figure 3: Inner structure imaging for OC
192
The simulation shows that the simplest objects
have shadows with transient characteristics, half-
dooms, and distortions of the type of crater, where
there are generally flat irradiating planes.
Changing the irradiation angle changes the
shadow to unrecognizability. To accurately
identify the intended OC, it is necessary to
automate the process of recognizing shadows,
taking into account possible distances between the
Figure 4: Object of complex shape
source, the OC and the screen receiver, the
irradiation angles, etc. Methods of analytical Objects marked with stars are not graphs, but
modeling of the OC with different shapes, drawings illustrating examples of corresponding
geometrical dimensions, foreshortening, graphs with the following input data: k=10; a1=2;
substance, and appropriate extinction coefficients, b1=4; c1=1; d1=1; d2=1; 1 = 1;a2=1; b2=5; c2=
3; 2 = 1 . The following formulas calculate the
used to develop procedures for identifying
dangerous objects under security supervision of
imaginary distribution of the visualized parameter
passengers and baggage, allow imaging of OC's
inner structure. To verify the developed models with logarithmic gain.
b(x, h ) = artg
multidimensional spectra of visualization images h
are obtained. The procedure for image processing x
consists of using a shadow of the object of a given
X 2(x, h ) = x 2 + h 2
shape to construct a two-dimensional spectrum
and its subsequent use in developing the standard b1
g1 = atg
spectral detector proposed in the research. This a1
detector is invariant concerning the location of the b2
OC in the working area. The invariance of the g 2 = atg
calculated spectrum to the location of the OC on a2
the plane of the screen provides the possibility of
applying algorithms for the calculation of two-
dimensional spatial spectra of the visualization
image about the wanted images of some image
anomalies in the endoscopic imaging systems of
the AvSS. That is, the desired density distribution
of the object of control μ(x, y) must be matched to
fit its two-dimensional spatial spectrum—Fourier-
image М ( К x , K y ) . In the further processing of Figure 5: Image of the internal structure of a
complex OC with applied parameters in three-
visualization data, we find solutions in the
dimensional form
frequency space М ( К x , K y ) , and then, through
Projective image OK with logarithmic gain.
the inverse Fourier transform, the desired
distribution is calculated μ*(x, y), . The resulting
distribution is selected according to those images,
which are in the memory of the supervision
system. A decision is made to detect a particular
object after matching the resulting image μ*(x, y)
and mask μ*(x, y).
Figs. 4 and 5 shows the spectra of images of
different shades of opaque OC of a simple shape
on the size of a 100×100 screen plane located
almost above the center of the screen.
Figure 6: Projective image of OK with logarithmic
amplification
193
The function corresponding to the intensity
distribution of the received radiation is obtained
by the formula:
d ( x, h ) = e − a ( x , h )
Figure 7: Image of the internal structure of a
complex OK with applied parameters, in three-
dimensional form
Consider the object of control in the form of a Figure 9: Image of the internal structure of a
pipe segment with a cavity inside. complex control object with applied parameters,
Let’s set the parameters of the rendering in three-dimensional form
system:
r1 = 3, r2 = 2, k = 10, d = 1, c = 1, a = 1
where k is the distance from the radiation source
to the screen; c is the distance from the radiation
source to the OK; r1 is radius OK; r2 is cavity
radius; d is layer thickness; a is the radiation
attenuation coefficient in the OK material.
The actual distribution of the extinction
coefficient will be:
(
0 if − x 2 + y 2 r1 x 2 + y 2 − r 2 − x 2 + y 2 x 2 + y 2 r 2
F1(x, y ) =
)
d
Figure 11: Shadow (a) of a parallelepiped and its
spectrum (b)
Figure 8: Object Figure 12: Shadow (a) of the cylinder and its
F1 r 2k spectrum (b)
OD 2 =
c+d .
On one plane, the shadows of two
X 2(x, h ) = x + h , OD2 = r 2k ;
2 2 parallelepipeds are located, and their spectral
c+d images are obtained (Fig. 13).
a(x, h ) = (a p (x, h ) − av (x, h ))a .
194
probability of correct detection of a signal from an
OC.
Figure 13: Shadows of two parallelepipeds and
their spectrum: a) shadows of two
parallelepipeds; b) a three-dimensional image of
Figure 15: Model of shadow OC
the spectrum of those shadows; c) a two-
dimensional projection of the spectrum of A mixture of useful signals and noise is shown
shadows of parallelepipeds in Fig. 15. A mixture of signal with noise in cases
of signal-to-noise ratios equaled to 2 (a) and 0.5
The following figures show the spectral (b) The Neyman-Pearson criterion is applied for
images of the shadows of the parallelepiped and optimal detection of an OC. According to the
the spheres that were located in space (Fig. 14). Neyman-Pearson criterion, the threshold level V
is determined from the condition that the
probability of a correct detection D with the given
probability of false alarm F was maximal. Hence,
the optimal character of the Neyman-Pearson
criterion is that it maximizes the probability of
correct detection at a fixed probability of false
alarms. In addition, it should be noted that the
Figure 14: Shadows of parallelepiped and program calculates the characteristics of the
spheres and the spectrum of their compatible detection. An example of these characteristics is
shadows: a) shadows; b) a three-dimensional shown in Fig. 10. On these graphs it is seen that
image of the spectrum of those shadows; c) a two- when the decision threshold is reduced, the
detection characteristic is more efficient,
dimensional projection of the spectrum of shadows
however, the probability of false detection is
Analysis of the spectra of hazardous and forbidden
increased. The analysis shows that the developed
OC allows us to create an appropriate database for spectral detector has good detection
the further detection of OC of various shapes and characteristics even at low signal-to-noise ratios.
complexity
When using X-ray systems to provide
automation of care and increase the reliability of
decision-making on the presence of prohibited
articles and substances in the OC, there are problems
in identifying different forms and locations of the
OC. For this purpose, the example of the spectral
detector model was constructed in the Matlab
environment. In this case, the detection occurs
regardless of the OC location and regardless of its
shape and size. The considered models are the
shadows of two objects in a field with specified Figure 16: Characteristics of signal detection for
boundaries. One object is a regular square (this kind
sample size 1000 and probabilities of false alarms
can have dynamite), and the other is a model of the
F = 0.05 and F = 0.03
machine gun (Fig. 15). Also, white Gaussian noise
and a mixture of image and noise are modeled
(Fig. 16). The developed program allows us to detect 4. Conclusions
an OC with a given probability of false alarms for
the corresponding threshold decision depending on The analysis of scientific publications has
the size of the OC. The program calculates the shown that the most effective methods for the
detection and identification of hazardous OCs are
195
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