IRJET- Intelligent Image Recognition Technology based on Neural Network

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4961
Intelligent Image Recognition Technology Based on Neural Network
Gopal Krishna Kushawaha1, Mr. Sunil Awasthi2
1M.Tech. Student, Department of Computer Science Engineering, Rama University, Uttar Pradesh, Kanpur
2Asst. Professor, Department of Computer Science Engineering, Rama University, Uttar Pradesh, Kanpur
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Abstract - Picture handling and acknowledgment is completed on the real picture change and change in order to
accomplish the point of recognizable proof. As a result of the normal for the picture data is that it is a two-dimensional
space, so the measure of data it contains is exceptionally enormous. Neural system picture acknowledgment innovation is the
cutting edge PC innovation, picture preparing, man-made consciousness, design acknowledgment hypothesis built up
another sort of picture acknowledgment innovation. Before the picture acknowledgment need to utilize advanced picture
preparing methods for picture preprocessing and include extraction. With the hypothesis of computerized reasoning
exploration and the advancement of PC innovation, the utilization of neural system in picture design acknowledgment
investigate is progressively dynamic.
Keywords: Image Recognition, Neural Network, Image Processing
Introduction:
The picture acknowledgment innovation is firmly identified with public activity, picture acknowledgment innovation is a
significant part of PC vision, neural system picture acknowledgment innovation is alongside the advanced PC innovation,
picture preparing, man-made reasoning, and example acknowledgment hypothesis built up another sort of picture
acknowledgment innovation [1]. To understand the acknowledgment of pictures, the first to get relating picture by picture
obtaining gadget, with the goal that the computerized picture; Then the picture acknowledgment, and its different data. In
this paper, neural system is utilized to examine the obtained advanced picture acknowledgment, the BP neural system is
brought into picture acknowledgment field, and joined with traditional computerized picture preparing innovation;
discover a sort of solid precision plane picture acknowledgment technique [2].
Picture acknowledgment includes a ton of data task, requiring high handling rate and acknowledgment accuracy, constant
and adaptation to non-critical failure of the neural system as per the necessities of picture acknowledgment. At first, this
paper investigates the conventional picture acknowledgment technique, going for the impediments of customary
strategies, and the mind boggling circumstances, for example, pictures show diverse state, during the time spent picture
preparing calculation for the picture division study and its improvement; in the meantime, as per neighborhood least
estimation of the issues existing in the BP neural system, improve the productivity of the system, improve the accuracy of
picture acknowledgment, and during the time spent system preparing utilizing the strategy for versatile learning rate
change, diminishes the system number of preparing and the preparation time. Utilizing the improved BP neural system
calculation for rotational twisting picture situating and acknowledgment, the improved calculation will be mix of extra
force and versatile learning rate, viably smother the system into a neighborhood least point, improves the preparation
speed of system. At long last approved through the test, demonstrated the achievability and adequacy of the streamlining
strategy, and acknowledged by programming, accomplish better outcomes.
Summary of the BP neural network:
BP arranges is multilayer feed forward neural system dependent on BP calculation, it is D.E.Rumelhart, J.L.McCellnad and
their group considered and structured in 1986 [3]. At present, in the functional utilization of counterfeit neural system, by
far most of the neural system model is the variety of the BP system and its structure, it is likewise a previous center to the
system, and typifies the most quintessence part of the fake neural system. BP neural system is a sort of multilayer forward
system, comprises of info layer and yield layer, concealed layer or multilayer structure, a sort of run of the mill three layer
BP neural system model is appeared in figure 1.

f(·)
f(·)
f(·)
f(·)
f(·)
f(·)
j
k
Y
kp
Ykp
Figure 1. Typical BP neural network model
The change capacity of shrouded layer as a rule as a non linear capacity. Change elements of yield layer can be non linear,
can likewise be straight, by the information, yield mapping needs [4]. Back spread calculation is the primary thought of the
learning procedure is isolated into two phases: organize 1 (positive proliferation), input data from the info layer to begin
well ordered ascertaining genuine yield estimations of every unit of each layer of neurons state just influence the following
layer of neurons state; Phase 2 (back engendering) process, if the yield layer neglects to accomplish the ideal yield
esteems, the layered recursive computation of the distinction between real yield and wanted yield esteem, as indicated by
the mistake remedy layer loads before make the blunder sign to a base. By consistently toward the mistake work is with
respect to the slant down on system loads and deviation change and moving toward the objective. Each weight esteem and
the mistake change corresponding with the impact of system blunder.
Intelligent image recognition process analysis:
Picture acknowledgment as a part of picture innovation has been a hot research theme in the field of picture handling and
example acknowledgment. Conventional example acknowledgment strategies connected to the shade of the picture
acknowledgment is for the most part dependent on picture highlights, shape and surface attributes of picture correlation,
as per the likeness between the factual qualities of picture assessment [5]. Neural system picture acknowledgment
innovation is the cutting edge PC innovation, picture preparing, man-made consciousness, design acknowledgment
hypothesis, built up a sort of new picture acknowledgment innovation, is in the conventional picture acknowledgment
technique dependent on combination of neural system calculation is a strategy for picture acknowledgment. In content
picture handling for instance, the character picture preprocessing is intended to make character pictures all the more
clear, the edge is increasingly self-evident, and each character division for highlight extraction. For this article chooses the
character pictures, picture preprocessing, the general stream graph is appeared in figure 2.
Figure 2. Flow chart of character image pre-processing

Highlight extraction can be thought of as an extraordinary sort of picture information disentanglement, its motivation is to
extricate the high-dimensional information contains the fundamental data of the low dimensional information viably, is
beneficial for the picture division and acknowledgment. As a rule, ought to have the attributes of a decent notice ability,
great unwavering quality, and autonomy and less number of these four attributes. The conventional element extraction
technique dependent on picture recurrence area attributes, including Fourier change, wavelet change calculation, and so
forth. Research in this paper to recognize the pictures, to get the picture subsequent to pre processing for the Euler
number of highlight extricating, character picture lattice of each line and every section dark pixels to the total of these two
attributes.
The intelligent image recognition application based on neural network:
This paper, it is by utilizing neural system to mutilation picture acknowledgment are examined, first with a CCD camera to
distinguish target picture data, gathered simultaneously, change the direction of the camera, gathered contortion picture
data of the in all likelihood focus on, the objective data bending beyond what many would consider possible completely,
the objectives of a wide range of twisting of picture data, make up the example library of target acknowledgment. At that
point, the example in the library picture is changed over to a computerized picture input mode - number for PC, advanced
separating preparing, will be some clamor and channel out superfluous data. At long last, the advanced data input test
picture structure for preparing the neural system learning, create picture acknowledgment arrangement of neural system.
Acknowledgment, picture with CCD camera's picture acknowledgment, after form - computerized transformation,
separating preparing contribution to distinguish the system framework, the framework will be ongoing determined
outcomes. This paper for the most part contemplated the rotational bending picture acknowledgment, as appeared in
figure 3 for the 36 tests pictures of character 'A', numbered 0 to 35, each example picture pixels is 40 x 40, and 10thusly
clockwise 10 °.
Figure 3. 36 sample images of character ‘A’
According to the requirements of identification, the design of three layers BP neural network, the number of hidden
layer units according to the improved BP formula to select which m for the output neuron
number, number n for the output neurons, a is an integer between 1-10, determine the structure of the network of 1600
* 45 * 36;After the normalization of image information processing input network By combining the additional
momentum and adaptive rate of the improved BP algorithm, the intermediate transfer function as Tansig and Logsig
function, training target error was 0. 001, momentum constant 0. 8, the initial vector adaptive for 0. 01, vector to
increase the ratio of 1. 05, reduce the ratio of 0. 7. Training process is shown in figure 4.

Figure 4. Training process of neural network
For illustrative purposes, this paper just picked 36 test picture in the trials, the real work, to improve the
acknowledgment precision, can pick the 360 examples of each at least 10, particularly for certain objectives to the
bending of the consistency of the restricted time, can incorporate the majority of its twisting in the preparation test, for
the advanced PC handling speed, a huge number of tests can likewise be a decent treatment and estimation, for example,
including distinctive extent in the example scale, the mutilation of picture, additionally can accomplish the objective of
profoundly situating. Exact acknowledgment and course of the objective, high situating for modern generation, logical
research and has significant hugeness in such angles as national security.
Conclusion:
In this paper, neural system is utilized to actualize the ID of rotational bending picture. So as to improve the preparation
speed of neural system and increment the unwavering quality of the system, by utilizing the energy and learning rate
versatile change of the improved calculation of joining the force technique diminishes the affectability of the system for
the neighborhood subtleties of mistake bended surface, adequately smother the system in a nearby least, upgrades the
accuracy of target ID; The versatile learning rate alteration viably abbreviates the preparation time. The exploratory
outcomes demonstrate that picture acknowledgment technique dependent on neural system is compelling and possible,
with the improvement of PC innovation and man-made consciousness hypothesis, the picture acknowledgment
innovation in target following, journey control, keen instrument, robot vision, and different fields will have wide
advancement and application prospect.
References
[1] G.Hinton, L. Deng, and D. Yu:Signal Processing Magazine, Vol. 29 (2012) No.6,p.82.
[2] E.S.A. Dahshan, T.Hosny, and A.B.M. Salem: Digital Signal Processing, Vol. 20 (2010) No.2, p.433.
[3] M. Karabatak, M.C. Inc: Expert Systems with Applications, Vol. 36 (2009) No.2,p.3465.
[4] O. Mendoza, P.Melin, and G.Licea: Information Sciences, Vol. 179 (2009) No.13,p.2078.
[5] S. Ji, W.Xu, and M. Yang: Pattern Analysis and Machine Intelligence, Vol. 35 (2013) No.1, p.221.

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