Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data
Предложены эквивалентностные пространственно-инвариантные алгоритмы распознавания полутоновых изображений идентификационных объектов, представленные набором символов, и результаты экспериментальных исследований. Показаны интерфейсы программы и результаты ее тестирования, подтверждающие высокое быстр...
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Krasilenko, V.G. Nikolsky, A.I. Bozniak, Yu.A. 2015-06-16T14:38:33Z 2015-06-16T14:38:33Z 2013 Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data / V.G. Krasilenko, A.I. Nikolsky, Yu.A. Bozniak // Управляющие системы и машины. — 2013. — № 4. — С. 12-19. — Бібліогр.: 18 назв. — англ. 0130-5395 https://nasplib.isofts.kiev.ua/handle/123456789/83175 004.896: 681.5 Предложены эквивалентностные пространственно-инвариантные алгоритмы распознавания полутоновых изображений идентификационных объектов, представленные набором символов, и результаты экспериментальных исследований. Показаны интерфейсы программы и результаты ее тестирования, подтверждающие высокое быстродействие и низкий процент нераспознанных символов. Equivalently space-invariant pattern recognition algorithms halftone identification of objects and the results of experimental studies are proposed. Interface of this software are shown. Results of its tests confirmed the high performance and low part unrecognized characters, less than 0,01%. Запропоновано еквівалентнісні просторово-інваріантні алгоритми розпізнавання напівтонових зображень ідентифікаційних об'єктів, що представлені набором символів, та результати експериментальних досліджень. Показано інтерфейси програми та результати її тестування, що підтвердили високу швидкодію і низький відсоток нерозпізнаних символів. en Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України Управляющие системы и машины Автоматическая обработка и распознавание изображений Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data Алгоритмы распознавания полутоновых изображений многосимвольных идентификационных объектов с использованием нелинейных эквивалентностных метрик и анализ экспериментальных данных Алгоритми розпізнавання напівтонових зображень багатосимвольних ідентифікаційних об’єктів з використанням нелінійних еквівалентнісних метрик та аналіз експериментальних даних Article published earlier |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| title |
Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data |
| spellingShingle |
Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data Krasilenko, V.G. Nikolsky, A.I. Bozniak, Yu.A. Автоматическая обработка и распознавание изображений |
| title_short |
Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data |
| title_full |
Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data |
| title_fullStr |
Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data |
| title_full_unstemmed |
Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data |
| title_sort |
recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data |
| author |
Krasilenko, V.G. Nikolsky, A.I. Bozniak, Yu.A. |
| author_facet |
Krasilenko, V.G. Nikolsky, A.I. Bozniak, Yu.A. |
| topic |
Автоматическая обработка и распознавание изображений |
| topic_facet |
Автоматическая обработка и распознавание изображений |
| publishDate |
2013 |
| language |
English |
| container_title |
Управляющие системы и машины |
| publisher |
Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України |
| format |
Article |
| title_alt |
Алгоритмы распознавания полутоновых изображений многосимвольных идентификационных объектов с использованием нелинейных эквивалентностных метрик и анализ экспериментальных данных Алгоритми розпізнавання напівтонових зображень багатосимвольних ідентифікаційних об’єктів з використанням нелінійних еквівалентнісних метрик та аналіз експериментальних даних |
| description |
Предложены эквивалентностные пространственно-инвариантные алгоритмы распознавания полутоновых изображений идентификационных объектов, представленные набором символов, и результаты экспериментальных исследований. Показаны интерфейсы программы и результаты ее тестирования, подтверждающие высокое быстродействие и низкий процент нераспознанных символов.
Equivalently space-invariant pattern recognition algorithms halftone identification of objects and the results of experimental studies are proposed. Interface of this software are shown. Results of its tests confirmed the high performance and low part unrecognized characters, less than 0,01%.
Запропоновано еквівалентнісні просторово-інваріантні алгоритми розпізнавання напівтонових зображень ідентифікаційних об'єктів, що представлені набором символів, та результати експериментальних досліджень. Показано інтерфейси програми та результати її тестування, що підтвердили високу швидкодію і низький відсоток нерозпізнаних символів.
|
| issn |
0130-5395 |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/83175 |
| citation_txt |
Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrices and analysis of experimental data / V.G. Krasilenko, A.I. Nikolsky, Yu.A. Bozniak // Управляющие системы и машины. — 2013. — № 4. — С. 12-19. — Бібліогр.: 18 назв. — англ. |
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| fulltext |
12 УСиМ, 2013, № 4
Автоматическая обработка и распознавание изображений
UDC 004.896: 681.5
V.G. Krasilenko, A.I. Nikolsky, Yu.A. Bozniak
Recognition algorithms of multilevel images of multicharacter
identification objects based on nonlinear equivalent
metrices and analysis of experimental data
Предложены эквивалентностные пространственно-инвариантные алгоритмы распознавания полутоновых изображений иден-
тификационных объектов, представленные набором символов, и результаты экспериментальных исследований. Показаны ин-
терфейсы программы и результаты ее тестирования, подтверждающие высокое быстродействие и низкий процент нераспо-
знанных символов.
Ecvivalently space-invariant pattern recognition algorithms halftone identification of objects and the results of experimental studies are
proposed. Interface of this software are shown. Results of its tests confirmed the high performance and low part unrecognized charac-
ters, less than 0,01%.
Запропоновано еквівалентнісні просторово-інваріантні алгоритми розпізнавання напівтонових зображень ідентифікаційних
об'єктів, що представлені набором символів, та результати експериментальних досліджень. Показано інтерфейси програми та
результати її тестування, що підтвердили високу швидкодію і низький відсоток нерозпізнаних символів.
Introduction. The main perspective tendency of
development of information-computating systems
and computer technologies is making them intel-
lectual and similar to human thinking and percep-
tion. As the models of intellectual systems in the
field of neurophisiology, artificial intelligence the
following models of auto-processing nets are mainly
used: connection models, neural nets models, mo-
dels with parallel distributed processing. Basic
researches reveal a number of important general
principles besides spontaneous intellectual quali-
ties. Among those features we can mention the
principle of parallel processing of information on
all the levels of its processing (global system be-
haviour, considered as “intellectual”) and the con-
cept of active and not passive memory. The great
interest to neural model forces to revalue the fun-
damental theses in many fields of knowledge, in-
cluding computer techniques. Neural models of
brain activity and cognitive processes, most pro-
bably will cause perspective results in neurophy-
siology, neurology, the advent of the systems with
increased computing resources and intelligent qua-
lities for solving problems of medical informatics
and diagnostics. Many failures on the way of im-
proving of artificial intelligence have appeared in
recent years as firstly of all, the chosen computing
techniques were not adequate to solve the impor-
tant and complicated problems, and secondly, simp-
le and not perfect neural models and nets were
applied. Today, rapid progress in mathematical
logic, especially matrix (multivalued, continuous,
fuzzy, neural) [1–6], accumulation of data regard-
ing continual (analog) and obviously nonlinear
functions of neurons [7–9], elaboration of the neu-
ral net theory, neurobiology and neurocybernetic,
and adequate algebrologic instruments for mathe-
matical description and modeling [6, 10–15], de-
velopment of optical technologies have created
conditions for construction of technical systems,
adequate almost to any problem of artificial intel-
ligence. The works [10, 16], and especially [11–14]
solve the problem of increase of capacity in artifi-
cial neural networks (ANN) and associate mem-
ory (AM), even in cased storing of greatly corre-
lated images, and the problem of convergence of
methods and training rules, using multilevel repre-
sentation of signals. The use of operations of neu-
ral logic operations – equivalence and nonequiva-
lence for construction of ANN and AM models is
common for works [11–14]. In this connection such
models and the theory were called “equivalental”.
УСиМ, 2013, № 4 13
They showed, and described negative and inhibit-
ting weights along with exciting ones at unipolar
and bipolar coding. Neural biologic (NBL) [1, 3,
10–12] is integration (gnoseologically development
loped and specified) of known logic: multivalued,
hybrid, continuous, fuzzy etc. At the same time, the
integrated operations in fuzzy logic are: operation
of fuzzy negation, t-norms and s-norms, and they
have the relation of dualism according to general
form of De Morgan principle. The example of
t-norms are: logical multiplication (min (a, b)),
algebraic multiplication (a·b), limited multiplica-
tion etc. The example of s-norms are: logical sum
(max (a, b)), algebraic sum (a + b – a·b), limited
sum (1(a + b)), contract sum etc [15, 17]. The
basic operations of NBL, used in equivalental
models NNAM [10–13], are binary operations of
equivalence and nonequivalence, which have a few
variants [12]. The variants of these equivalence
operations on a carrier set ]1,0[
uC are shown on
fig. 1 (a,b,c) respectively for:
)},min(),,max{min(~1 bababaeq ;
bababaeq ~2
;
babaeq 1~3 (1)
baeq ~1 baeq ~2
0
0,5
1
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
a
a
eq1
0
0,60
0,2
0,4
0,6
0,8
1
E2
b
eq2
baeq ~3
0
0,5
1
0
0,2
0,4
0,6
0,8
1
E3
c
eq3
Fig. 1. Operations of equivalence
Their negations are first, second and third non-
equivalence respectively. Relation of these opera-
tions when unipolar and bipolar coding is similar
to relation of the signals and expressed as:
2/))/~((/~
(~)(~)
bbuu baDba , where
(~)
/~ is mean-
ing valid for any of these operation variables, be-
ing:
uuu Cba , ],0[ D , ],[, DDCba bbb .
In general case for scalar variables Cba ,
= [A, B] – continuous line segment, signals them-
selves and their functions and segment C can be
brought to segments DD, (or [–1, 1]) in bipolar
coding and D,0 (or [0, 1]) in unipolar coding.
When shifting on interval, corresponding to C =
= [A, B], et at the value (i.e. when C = [A
, B ])) set is chosen) the above mentioned
equivalence and nonequivalence operations over
variables
Cbbaa ^ , are expressed over
the simple addition to equivalences and nonequi-
valences from non-shifted variables the shift ,
namely: )~(~ baba ;
)~(~ baba .
Besides: for k > 0, )/~(
(~)
bak )/~(
(~)
bkak .
Thus, equivalence (nonequivalence) of signals do-
esn’t depend on constant shift (analogy to constant
component), on scale factor k, on bipolar or uni-
polar coding. These operations are the generaliza-
tion of XNOR and XOR operations of binary logic
and allow logical comparison of continuous-level
(analog) and multilevel signals with unipolar and
bipolar representation, including scalar, vector and
matrix. For that purpose normalized equivalence
and nonequivalence of vectors and matrices are
used [11, 12, 14], which for these two matrices
A =
JIija
, B =
JIijb
JI ]1,0[ are determined
in the following way:
J
j
ij
I
i
a
JI 1
ij
1
n b~1~ BA ; (2)
J
j
ij
I
i
a
JI 1
ij
1
n b/~1/~ BA . (3)
Normalized equivalence n~ and nonequivalence
n/~ – are more general new complementary met-
14 УСиМ, 2013, № 4
rics in metrical space R. In particular,
n/~ is a
normalized distance JId /),(1 BA between ma-
trices and for N1,0, BA it turns into norma-
lized distance NdH /),( BA of Hamming. As it can
be seen from expressions (2) and (3), component
and component operation of NBL (~) or ( /~ ) is
generalized for matrix case, and NBL logic be-
comes matrix NBL (MNBL). The variants of op-
erations (~),( /~ ) depend on different types of
t-norms and s-norms operations used in them and
integrated fuzzy-operations of union and intersec-
tion [17]. Depending on the type or variants of
equivalent algebra (EA) [11, 12] we can offer new
algebro-logical instruments for creation of equiva-
lental theory on the basis of MNBL. In works [11,
12] new notions of equivalental unidimension (1-
D) and twodimension (2-D) functions were intro-
duced:
N
i
ii ba
N
bafE
1
)~(1),()(~
;
.)~(
*),(~),(~
1 1
,.
~
N
n
M
m
nnmn ba
fE BABA
(4)
In this equation symbol ( ~ ) denotes the convo-
lution (correlation) with operation of “equivalence”.
Normalized space-dependent equivalence and
nonequivalence functions were introduced in the
work [14]
JI /~~ BAe and
.1,0
~][/*
)1)(1(
,
/~/~
JMINne
JI e1BAe
These functions e~ and ~e reflect the measure
of equivalence and nonequivalence of two images
depending on their mutual spatial shifting. The
connection of functions E~ (, ) with correlation
functions (linear LCF and nonlinear NCF) was
demonstrated [11, 14]:
JIJI /*/*
~
BABAe
);()( B,ABA, nn LCFLCF (5)
)()(
~
B,ABA,e nn LCFLCF
; (6)
.)()(
)/()**(
)/(*~~
minmin
B,ABA,
BABA
BAe
nn FCNFCN
JI
JI
(7)
On the basis of these functions more genera-
lized modified matrix-tensor neurological equiva-
lental models (MTNLEMs) for space-invariant re-
cognition of 2-D images were synthesized [14].
Making use of formulae transformation for cal-
culation of linear and nonlinear correlative func-
tions and criterial functions of mean absolute error
(MAE) and mean square error (MSE), authors show
in work [18] that these formulae can be reduced to
two groups of mathematical constructions. These
constructions determine two groups of architectu-
res of parallel action: high-speed correlators with
nonlinear and image morphological processing. In
practice there often appears the problem dealing
with recognition of multilevel images of the ob-
jects being identified; on special seals, on engine-
ering objects designed for various applications, do-
cuments etc. That is why, taking into account the
above mentioned facts and possibilities of equiva-
lent functions applications shown earlier, we will
suggest in the given work the recognition algo-
rithms based on nonlinear equivalent metrics and
will show the results of investigation of these al-
gorithms.
The algorithms of recognition without pre-
liminary segmenting of input image (the algo-
rithms of group I)
The idea of NLEАs is concluded in segmenting
(on the base of a priori information) into frag-
ments, corresponding to separate symbols, source
image of multicharacter object or reference image,
composed of a set of alphabet of symbols, sub-
jected to recognition. For each ith segment of
source image nonlinear equivalence functions
with general reference image for the first group of
NLEAs realization variants are calculated. For the
УСиМ, 2013, № 4 15
second group of NLEA realization variants NLEFs
for each ith reference image segment with the ge-
neral input multicharacter image are calculated.
Inside of each NLEА group we apply several mo-
difications of NLEFs, depending both on the type
of "equivalence" ("nonequivalence") being used
and on parameters, determining the type of non-
linearity and a number of autoequivalence trans-
formations. Moreover the latter can be applied both
to separate components of images being compared
and to integrate NLEF-evaluations and normalized
NLEF as a whole. Let dimensions (in the number
of pixels) K (in vertical position) and L (in hori-
zontal position) in rectangular fragment Р of the
image be chosen in such a way, that each of pos-
sible Q reference images of
)10( Qq
S symbols from
selected alphabet could be paced in fragment re-
gion (most closely and with minimal dimensions
of rear plan). Then multi level image JIija ][A
to be recognized of multisymbol (R-symbol) iden-
tification object О with horizontal row wise ac-
commodation of symbols will include all R of
)10( Rr
P fragments, each of which can be the image
of one qS . of symbols. Total dimensions I, J of the
image A must be greater than values K and R·L
accordingly and meet the requirements: I = K + d1;
J = R·L + d2, where d1 and d2 – total amounts of
pixels in vertical and horizontal positions accord-
ingly complementing the interfragmental space till
dimensions A. Fig. 2,а shown initial multilevel
(256 levels )2550( ija ) image A = {0,255}IxJ, to
be recognized, images of arranged set (alphabet)
of symbols LK
lkq S 255...1,0][ ,S (see fig. 2,b).
In this case, criterial function (space-dependent)
)1()1(
, ][),( LJKI
q
q b B can be determined for
each qS reference, as it is shown in fig. 2,с.
Knowledge algorithms regarding the number of R
symbols in identification object (number of frag-
ments in the image A) and possible deviations of
symbols positions taking into account the gaps
permit to divide the image of two-dimensional
criterial function into R regions. In each R-th re-
gion there is an extremum (min or max) of crite-
rial function qB namely qr
max(min)B and its coordi-
nates. Comparing all q
max(min)B within r-th region,
we can find index q’ (number of symbol referen-
ce), which gives us the greatest (the least) value of
criterial function in this region. Thus we deter-
mine (recognize) symbol-reference in each r-th
region, and using the coordinate )(, ',', qrqr yx of the
extremum of the best equivalence (similarity) or
nonequivalence (nonsimilarity) the position of
recognized reference symbol in the object is de-
termined, and coordinates of the following region
(sign location) are being searched.
For the analysis of NLEF functions obtained
after the first step, both of integral signs and for
decision taking based on their collection, we
perform transformation of 2-D NLEFs into 1-D
NLEFs, carrying out by component operations
of minimum (maximum) over vector arrays, cor-
responding columns (or lines) of initial 2-D
NLEFs. Thus we select local spatial extremums
of NLEF. Such compression of information is
possible and reasonable and permits to simplify
considerably further analysis of recognition. On
the following step by the result of comparisons
of 1-D transformed NLEF local extremums
(lighting countings out) (number of which cor-
responds to a number of reference recognized
symbols), we come to a conclusion on the pres-
ence or absence (the most probable) of one of
the reference symbols and on its coordinate lo-
cation in initial image being recognized. The
modification of the given algorithms is the algo-
rithms, when criterial functions are calculated
only in R -th local regions ( RR ' )in the
proximity of arising determined possible posi-
tion of the extremum. It considerably decreases
temporal expenses needed for its realization.
Formula for calculation of maximorum (mini-
morum) of criterial function (q-th) in R region is
the following:
)(
(min)
max r
qr xB ,...,(max
10 ,,(min)
q
yx
rq
yx
r
rr
bb ),
1,
q
yx
r
KIr
b
.
Formula for q selection is the following:
qq , if q
x
rQq
x
rq
x
rq
x
r
rrrr
Bbbb ),......,(max 110
(min)
. (8)
16 УСиМ, 2013, № 4
Fig. 2. Formation process of criterial functions
Recognition algorithms applying the method
of preliminary segmentation of input image
(algorithms of group II)
First step: Image Р0 is formed as arranged set
of all reference symbols:
))((
,,0
1
01
0
21 dLQdK
ji
Q
q
q
Q
i
i P
SPP . (9)
Second step: The image to be recognized
A JI 255,0 is segmented into R sections, re-
gions (sign places of symbols) having the dimen-
sions of I J pixels taking into account a priori
information-regarding the location of symbols and
possible deviations.
Third step: Criterial functions ),(),( 0PAB rF
for each rth segment of input image A and Р0 (as
the set of references) or criterial functions
),( 0PA qrF are calculated for each rth segment Ar
and arranged set qth conventional region Р0,
namely qP0.
Fourth step: For each segment Ar
(its serial
number) the number q of conventional region of
reference arranged set Р0, is determined, this
number gives the greatest (the least) value of cri-
terial function for the best special mutual shift. By
this number q the recognition of rth segment of
identification object image is carried out (com-
puter code or reference symbol is put on the posi-
tion, coordinate of rth segment).
Criterial functions
For all possible 3 groups of algorithms we ap-
ply the following criterial functions from two im-
ages NM
ija ][A and LK
klp ][P , where aij, ,0{klp
1,, 255}, M > K, N > L:
a)
1
0
1
0
,,,,
,
255
)255)(255(1
)(
K
k
L
l
lklklklk
a
papa
LK
B
, (10)
moreover
)(0 KM ; )(0 LN ;
b)
,)255,255min(
),min(2551
),min(),max(1
1)(
,,
1
0
1
0
,,
1
0
1
0
,,,,
1
0
1
0
,,,
lklk
K
k
L
l
lklk
K
k
L
l
lklklklk
K
k
L
l
lklkb
pa
pa
LK
papa
LK
pa
LK
B
(11)
Fig. 3. Part of lD NLEF function )(7,5,2,1
max
2,1 qr
b B (Xr=1= 8, Xr=2= 26)
a Input recognized multilevel image
b Equivalence function (with character “1”)
c Dependency of function )(max r
qr
c XB
Fig. 4. Character «1» recognition processes are shown
УСиМ, 2013, № 4 17
c) )]([)( ,, bf anc B , (12)
d) )]([)( ,, bf bnd B , (13)
where nf – nonlinear function used in work [14]
and named “autoequivalence”
aafn ),(
;5,0,/~.../~
;5,0,~~
...
aforaa
aforaa
timesa
timesa
(14)
e) ]([)( ,,,, lkne apf B . (15) (15)
In criterial function ),( Be nonlinear trans-
formation is performed over the whole set of sam-
ples, obtained before addition over the region of
indices k, l change.
Formation of reference images of symbols
Unlike the recognition algorithms suggested
before, which are based on equivalence models
of neuronets [14], input images being recognized
and images of reference symbols (while teaching
and input) are not converted into the set of two-
grading images, but are processed in the initial
gray scale format. In order to form reference
symbols, mathematical expectations are determi-
ned from the sets of representatives for each qth
symbol. Averaged on several realizations frag-
ments of separate symbols, were used for shaping
of reference base and reference general image.
Taking into account slight possible shifts the in-
distinct (blurring) representation of each qth sym-
bol is formed.
Computer modeling and the results
For the selection of the best optimal criterial
function, taking into account the influence of va-
rious noises and interference, distorting the image
being recognized, selection of the needed algo-
rithm from the group of algorithms, we have de-
veloped the program aimed at modeling of sug-
gested recognition algorithms. The suggest pro-
gram permitted to establish the needed, form, al-
gorithm type, criterial function, display the results
(final and intermediate) in graphic interface (see
fig. 5), suitable for researcher. Image, being recog-
nized, in particular, of 16446 pixels of dimensi-
onality were coded by 256 gradation levels, presen-
ting 7 symbol identification number of the object.
Other images were also used, for instance, 14-sym-
bol object with spaces etc. In the first case dimen-
sionality of reference image was 3018 pixels.
Fig. 5. Graphical interface of research program for recognition
algorithms simulation
Deviation from forecasted distance between
symbols at segmenting and processing we choose
±10 pixels. On image being recognized we super-
imposed a noise with different parameters and for
each value of parameter and each type of NLEFs
1D-transformed NLEFs were computed, corres-
ponding to all possible references. Fig. 3, 4 showed
2D and 1D NLEFs. When increasing a parameter,
characterizing the degree of nonlinearity in 2D
NLEFs, type of the latter can be controled. NLEА
allow recognize under noising up to 50% (normal
law). Correlation of peaks to lateral petals can be
increased, selecting the type of function. Noised
multilevel images and recognition results are shown
by fig. 6. Consider the possible hardware imple-
mentation. For calculation of criterial functions
equivalentors of images, introduced in [11, 12] can
be used. They are correlators (convolution opera-
tion systems) realizing linear LCF and nonlinear
NCF space-dependent functions from source and
additional images (see (5)(7)). The realization of
such devices is described in [14, 18] For calcula-
tion of nonequivalent functions the same devices
are used, but while writing in LCTV two images
are written (one original image and the second –
complementary image) in accordance with for-
mula (6). The important problem is the deve-
lopment of parallel operating real-time high per-
18 УСиМ, 2013, № 4
formance digital convolvers and correlators, func-
tioning in signal region. This problem will be re-
ported in further publications. To test the proposed
approach has been developed a real program for
the rapid identification numbers on buildings
locking sealing devices. The program production
tested at the state enterprise "Vіnnitsatransprilad"
South-Western Railway. The system has a number
of advantages: 1) Enters the 20 images on the
bodies of locking sealing devices with a matrix
that is installed on the scanner; 2) Recognize the
images invariant to shift and turn on the bodies of
locking and sealing devices, and not sensitive to
shift and rotation of the buildings themselves
locking sealing devices in the matrix; 3) Signals
of images to recognize bad and does not recog-
nize at all; 4) Recognize the 7 digits on the image
and exports them to bill submitted machine codes;
5) Refer recognized numbers of locking sealing
devices with numbers already in a database;
6) Recognize the image of a seven-digit one set of
20 images of locking sealing device in 100-200
seconds; 7) Resistant to very noisy images of
locking sealing devices and lighting glare on
the surface.
Graphical interface of the experimental
manufacturing program for recognition al-
gorithms simulation are shown on fig. 7.
Results of computer modeling and laboratory
studies, conducted on real objects have con-
firmed advantages of such algorithms. Addi-
tional entering of correlation factors in rec-
ognition models improves the quality and
validity of NLE-recognition algoritms. As
our research shows, NLEА possess higher
discriminant properties than correlation and
other conventional algorithms.
When adding background component in
the image being recognized in reasonable
measues the character and quality of recog-
nition doesn’t change applying NLEA. As a
result of modeling the hypothesis that the
preliminary processing of images, containing
considerable level of noise, in particular,
seperation of sidebars does not improve but
worsens the quality of recognition.
Conclutions. The suggested nonlinear-
equivalent recognition algorithms of multilevel
images of identification objects possess good re-
cognition quality, especially if the objects to be re-
cognized are in noisy environment, if background
noises have been added. NLE-algorithms permit to
recognize, as it has been proved by lab tests, in
such noise conditions when traditional (correla-
Number of
recognized
characters Input images Noise,
% I group of
algorithms
II group of
algorithms
Diagrams
For ),( Bb
, noise – normal
20 7 7
40 7 7
50 7 7
60 7 7
70 7 7
80 7 7
90 7 7
0
1
2
3
4
5
6
7
20 40 50 60 70 80 90
Noise, %
N
um
be
r o
f r
ec
og
ni
ze
d
ch
ar
ac
te
rs
I groupe of algorithms II groupe of algorithms (modif ied)
For ),( Bb
, noise – by Gause
20 7 7
40 7 7
50 7 7
60 6 6
70 3 5
0
1
2
3
4
5
6
7
20 40 50 60 70
Noise, %
N
um
be
r o
f r
ec
og
ni
ze
d
ch
ar
ac
te
rs
I group of algorithms II group of algorothms (modif ied)
Fig. 6. Noised multilevel images and recognition results
Fig. 7. Graphical interface of testing program for recognition algo-
rithms simulation
УСиМ, 2013, № 4 19
tion) algorithms fail. The experiments have shown
that preliminary processing of images being rec-
ognized, in particular those, whose outlining, when
they are saturated with noise and decreased levels
number, does not lead to increase of recognition
quality. The results of theoretical research are im-
plemented in a software product designed for the
rapid identification numbers on the sealing de-
vices. The software product was tested on produc-
tion enterprise "Vinnitsatranspribor" South-Wes-
tern Railway. Interface of this software are shown.
Results of its tests confirmed the high perform-
ance and low part unrecognized characters, less
than 0,01%.
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Тел. для справок: +38 0432 519-249, +38 098 370-7440,
096 193-7836, 097 261-9784 (Винница)
E-mail: krasilenko@mail.ru, nikolskyy@i.ua,
it@minisoft.com.ua
© В.Г. Красиленко, А.И. Никольский, Ю.А. Бозняк, 2013
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/NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
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/TUR <FEFF005900fc006b00730065006b0020006b0061006c006900740065006c0069002000f6006e002000790061007a006401310072006d00610020006200610073006b013100730131006e006100200065006e0020006900790069002000750079006100620069006c006500630065006b002000410064006f006200650020005000440046002000620065006c00670065006c0065007200690020006f006c0075015f007400750072006d0061006b0020006900e70069006e00200062007500200061007900610072006c0061007201310020006b0075006c006c0061006e0131006e002e00200020004f006c0075015f0074007500720075006c0061006e0020005000440046002000620065006c00670065006c0065007200690020004100630072006f006200610074002000760065002000410064006f00620065002000520065006100640065007200200035002e003000200076006500200073006f006e0072006100730131006e00640061006b00690020007300fc007200fc006d006c00650072006c00650020006100e70131006c006100620069006c00690072002e>
/UKR <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>
/ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/ConvertColors /ConvertToCMYK
/DestinationProfileName ()
/DestinationProfileSelector /DocumentCMYK
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /MediumResolution
>>
/FormElements false
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles false
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /DocumentCMYK
/PreserveEditing true
/UntaggedCMYKHandling /LeaveUntagged
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false
>>
]
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [612.000 792.000]
>> setpagedevice
|