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Image Recognition and Processing using
Artificial Neural Network1Md. Iqbal Quraishi,
2J Pal Choudhury,
3Mallika De
1Dept. of IT, kalyani Government Engineering College,
[email protected]
2Dept. of IT, kalyani Government Engineering College,
[email protected]
3Dept. of Engineering and Technological Studies, University of
Kalyani, [email protected]
Abstract:
There are several techniques for image recognition. Among those
methods, application of soft
computing models on digital image has been considered to be an
approach for a better result. The
main objective of the present work is to provide a new approach
for image recognition using ArtificialNeural Networks. Initially an
original gray scale intensity image has been taken for
transformation. The
Input image has been added with Salt and Peeper noise. Adaptive
median Filter has been applied on
noisy image such that the noise can be removed and the output
image would be considered as filtered
Image. The estimated Error and average error of the values
stored in filtered image matrix as have been
calculated with reference to the values stored in original data
matrix. Now each pixel data has been
converted into binary number (8 bit) from decimal values. A set
of four pixels has been taken together
to form a new binary number with 32 bits and it has been
converted into a decimal. This process
continues to produce new data matrix with new different set of
values .This data matrix has been taken
as original data matrix and saved in data bank. Now for
recognition, a new test image has been taken
and the same steps have been applied to get a new test matrix.
Now the average error of the secondimage with respect to original
image has been calculated based on the both generated matrices. If
the
average error is more than 45% then a conclusion can be made
that the images are different and cannot
be matched. But if the value of average error has been found to
be less than or equal to 45%, an effort
has been made to use the artificial neural network on test data
matrix with reference to original data
matrix thereby producing a new matrix of the second image(test
image). The total average error has
been calculated on generated data matrix produced after the
application of artificial neural networks on
test data matrix. It has been observed that the value of average
error is less than that of test image
without application of artificial neural network .Further it has
been observed that the test image is
matching and recognized with respect to original image.
Keywords: Digital Image Processing, Artificial Neural Network,
The Feed forward back propagation
neural network , Average Error, Gray scale intensity Image.
1. Introduction.The main aim of image processing is to alter the
visual impact such that the information content improvesand as a
result the said image is more suitable than original image. This
technique helps in getting a better
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visibility of any portion or feature of interest of an image and
suppressing the information in other portionor feature of that
image. Image Recognition has been dedicated with finding the
identity of an object
being observed in the image from a set of known labels.
Different Recognition techniques are availablefor use but the
selection of an appropriate choice of such techniques depends
mainly on a given task athand and some other related parameters.
Soft Computing is an emerging field built of latest techniqueslike
fuzzy logic, artificial neural networks, evolutionary computation
and machine learning. Each soft
computing technique can be applied to produce solutions to any
problem that are too complex or noisy totackle with conventional
methods. This paper will provide a new approach for image
recognition and
processing using Artificial Neural Network. Artificial Neural
Network has been one of the recentdevelopment tools that are
inspired from biological neural networks. The main advantage of
this new
powerful tool is to use its capacity to solve problems that are
not very easy to be solved by traditionalcomputing methods.
The traditional computers use step by step approach in solving a
problem and each step should be welldefined and computable. The
computer cannot solve the problem if any step that the computer
needs tofollow is not known. So to solve a problem using a computer
need all knowledge of how to solve the
problem. But Artificial Neural Networks are new techniques that
follow a different way from traditional
computing methods to solve problems. Artificial Neural Networks
may be considered as much more
powerful because it can solve problems where how to solve have
been not known exactly. Uses ofartificial neural networkhave been
spread to a wide range of domain like image recognition,
fingerprint
recognition and so on. Artificial Neural Networks have the
capability to adapt, learn, generalize and
organize data. Some of the known structures of artificial neural
network are Percepton, Adaline,Madaline, Kohonen, Back
Propagation.
2. Related Work.The appearance of digital computers [1] and the
development of modern theories of learning and neural
processing both occurred at about the same time, during the late
1940s. The study of artificial neural
systems (ANS) [2] on computers remains an active field of
biomedical research. Since that time, the
digital computer has been used as a tool to model individual
neurons as well as clusters of neurons, whichare called neural
networks. Traditional techniques from statistical pattern
recognition were popular untilthe beginning of the 1990s.In the new
era, 2000, Robert P.W. Duin, and Jianchang Mao [3] gave us
aholistic summary and compared some well known methods in pattern
recognition system. The review was
mainly meant for statistical approaches. Artificial neural
network (ANN) was discussed there as a part.As it is found that
statistical methods are more or less suffer from unavailability of
general
mathematical methods for recognition of features. A new approach
for feature extraction based onthe calculation of Eigen values from
a contour was proposed and found that using feed forward neural
network satisfactory results was obtained [4]. Artificial neural
network s has increasingly been used as an
alternative to classic pattern classifiers and clustering
techniques. In the field of medical imageprocessing, Kenji Suzuki
[5] compared pixel based and non pixel based ANNs to show that the
formerapproach is much better when it comes to segmentation and
feature calculation. The paper also concludes
that Massive-Training ANNs (MTANNs) can be used to enhance
images. In 1993 review article on imagesegmentation, Pal and Pal
[6] predicted that neural networks would become widely applied in
image
processing. Segmentation, based on neural networks is found to
show rich capabilities [7]. Anotherrelated work in the domain of
medical image processing shows artificial neural network for
imagesegmentation. The approach was conjugated with real time
applications. A hybrid neural network was
proposed [8].This hybrid neural network shows that error rate,
when compared with Eigen face method,found to be producing
satisfactory results. A more real time approach in the direction of
the advancement
of artificial neural networks shows that, how the detection and
quantification of persons can be done incluttered beach scenes [9].
It shows neural-based classification system. An approach to perform
neutral
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facial image recognition using Parallel Hopfield Neural Networks
[10], shows encouraging results inrecognition rate. A survey based
on Hopfield neural networks was published in the year of
2007[11],
where a broad Theoretical review of the concept was presented.
Object recognition consists of locatingthe positions and possibly
orientations and scales of instances of objects in an image. The
purpose mayalso be to assign a class label to a detected object.
Some other types of ANNs like feed-forward artificialneural network
approaches can also be used for object recognition.
This paper aims to provide an alternative solution for object
Recognition using Artificial Neural Network.
Initially an original gray scale intensity image has been taken
as a reference and it is saved as originaldata bank. For processing
the method of transformation has been applied on the original
image. In thetransformation firstly each pixel data has been
converted into binary number (8 bit) from decimal values
as the input image is an intensity image. Then a set
offourpixels has been taken together to form a newbinary number
with 32 bits. Thereafter the binary number has been converted into
a decimal number. Thisprocess has been continued for whole image
row wise such that a new data matrix with different set of
values has been produced. This data matrix has been taken as
original data matrix and saved in data bankfor reference. Now a new
test image has been taken for recognition. The same steps have been
applied onthe new test image to get a new test matrix and then the
average error of the second image with respect to
initial image has been calculated based on the generated
matrices developed earlier. If percentage error is
more than 45% then a conclusion has been made that the images
are different and not matching and norecognition is possible. On
the other hand if the average error has been found to be less than
or equal to45%, an effort has been made to use artificial neural
network on test data matrix with reference to originaldata matrix
to produce a new matrix of the second image. The average error has
been calculated on
generated data matrix produced after applying Artificial Neural
Network on test data matrix. It has beenobserved that if the
average error is less than that of the value obtained earlier then
it has been concluded
that the images are matching and therefore can be
recognized.
A flow diagram for Image Recognition and Processing using
Artificial Neural Network has been
furnished in Figure -1
Start
Removal of Salt and Pepper Noise by using Adaptive median
Filter
Transformation of Test Image into Test data Matrix
Calculation of average error of test data matrix based on
original data matrix
Input Image effected with Salt and Pepper Noise
Input Original Image
Calculation of Average Error of the Filtered Image based on
Input Original Image
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Figure -1
Flow Diagram
3. Implementation
3.1Processing of Original Image.Step 1.
The initial optimal image has been taken as furnished in Figure
-2 which has been considered as originalimage.
Step 2.
The Input image has been added with Salt and Peeper noise.
3.2Processing of Noisy ImageStep 3.
Adaptive median Filter has been applied on noisy image such that
the noise can be removed and theoutput image would be considered as
filtered Image.
Step 4.
The estimated Error and average error of the values stored in
filtered image matrix as furnished in table 3
have been calculated with reference to the values stored in
original data matrix. The average error hasbeen found as 12%.
Step 5.
The original image has been transformed into data matrix
containing pixel values which have been
furnished in Table -1. For simplicity first 10X10 matrix
elements are shown.
Table -1
Training on test data matrix using Artificial Neural Network to
develop new data matrix
Calculation of average error of the new data matrix based on
original data matrix
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Input Data Matrix
126 152 171 172 171 173 162 143 117 116
124 155 176 175 173 174 159 135 119 117
121 154 175 171 170 175 162 136 120 118
129 160 178 171 169 177 165 141 120 119
122 151 171 170 169 173 161 139 119 119
71 97 124 139 148 152 145 134 120 12230 43 64 85 100 109 117 124
122 125
34 30 35 48 59 69 89 109 123 128
29 28 28 29 32 43 64 83 98 113
25 24 26 27 28 33 48 63 89 104
Step 6.
For easier calculation four pixels have been taken together. The
four pixels have been taken row wise andconverted into individual
binary numbers.
Step 7.
The binary values of four pixels together side by side have been
combined and formed as 32 bit binarynumber.
Step 8.
Now the 32 bit binary number has been converted into a decimal
number.
Step 9.
The decimal number as generated in step 5 has been placed in
original data matrix termed as ORMAT[][],which have been furnished
in table-2.
Table -2 Original Data Matrix ORMAT[][]
8296619 10005420 11250859 11316141 11251106 11379343 10653557
9401716 7697511 7628657
8166320 10203311 11579309 11513262 11382431 11444103 10454903
8877941 7828840 7694449
7969455 10137515 11512746 11250351 11186082 11510408 10651768
8943734 7894633 77602418495282 10531499 11709353 11250097 11121061
11642253 10849656 9271415 7894889 7825776
8034219 9939882 11250345 11184557 11120033 11379083 10586999
9140087 7829355 7826290
4678012 6388875 8162196 9147544 9738385 9998726 9537144 8812666
7895663 8023927
1977152 2834517 4216164 5596269 6581621 7173500 7699578 8157821
8027509 8222077
2235939 1975088 2306107 3160901 3884377 4544877 5860731 7175040
8093817 8419714
1907740 1842205 1842464 1908779 2108224 2834515 4215650 5464689
6451580 7437437
1644570 1579547 1710876 1776673 1843504 2175039 3161945 4151656
5859445 6845815
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Step 10.
The instructions furnished in step 3 to step 6 have been
repeated for the total pixel value of the originalimage as stored
in table 1. Therefore a matrix has been produced which has been
stored in data matrix
termed as ORMAT[][] as furnished in table-3. It is to note that
first 10X10 matrix elements are shown intable-3 for easier
presentation.
3.3 Processing of second Image (Test Image)
A new image has been taken which is considered as a test image.
Now it is necessary to check whether
the said image can be recognized or not. The test image has been
furnished in Figure-2.
Step 11.
Instructions as furnished in step 2 have been executed on test
image to generate a new data matrix asfurnished in Table-3.
Table -3 Test data Matrix
150 160 166 161 148 135 122 112 113 114
159 163 160 149 137 128 120 114 113 114
162 161 154 140 130 125 120 115 113 114
158 158 153 143 135 129 122 114 114 115
157 159 156 147 140 133 124 115 114 115
156 159 157 148 139 133 125 117 114 116
142 149 152 147 140 133 126 120 115 116
124 136 147 148 143 136 128 121 115 116
88 116 139 139 132 129 125 119 117 117
63 89 116 126 127 127 124 120 118 118
Step 12.
The four pixels have been taken row wise and converted into
individual binary numbers.
Step 13.
The binary values of four pixels together have been combined to
form 32 bit binary number.
Step 14.
Now the 32 bit binary number has been converted into a decimal
number.
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Step 15.
The decimal number as generated in step 11 has been placed in
test data matrix termed as TESTMAT[][],which have been furnished in
table -4.
Table -4 TESTMAT[][]
9871526 10528417 10920340 10589319 9734010 8878704 8024177
7369074 7434868 7500917
10462112 10723477 10524041 9800064 9011320 8419442 7893617
7500146 7434868 7500917
10658202 10590860 10128514 9208445 8551800 8222835 7893873
7565682 7434868 7500918
10395289 10393999 10063751 9406337 8880506 8485490 8024690
7500403 7500660 7566454
10330012 10460307 10261388 9669765 9209212 8748147 8156018
7565939 7500661 7566711
10264477 10460564 10327179 9735045 9143677 8748405 8222066
7697012 7500917 7632247
9344408 9803923 9999244 9669765 9209214 8748664 8288371 7893876
7566454 7632503
8161427 8950676 9671823 9736072 9406592 8945785 8419699 7959412
7566454 7632503
5797003 7637899 9145220 9143425 8683901 8486263 8222581 7828853
7697782 7698039
4151668 5862526 7634559 8290175 8355708 8354936 8157302 7894646
7763574 7763574
Step 16.
The instructions furnished in step 9 to step 12 have been
repeated for the total pixel value of the testimage to form a data
matrix termed as TESTMAT[][] as furnished in table-6.
3.4 Calculation of Average Error of test data matrix based on
original data matrix.
Step 17.
The estimated Error and average error of the values stored in
decimal matrix as furnished in table 3 havebeen calculated with
reference to the values stored in original data matrix. The average
error has beenfound as 31%. The Estimated errors have been
furnished in table -5.
Table -5 Estimated Error data for First 10X10 data
0.189825 0.052271 0.029377 0.064229 0.13484 0.219752 0.246808
0.216199 0.034121 0.016745
0.281129 0.05098 0.091134 0.148802 0.208313 0.264299 0.244984
0.155193 0.050323 0.025152
0.337382 0.04472 0.120235 0.181497 0.235496 0.285617 0.258914
0.15408 0.058238 0.033417
0.223654 0.013056 0.140537 0.163888 0.20147 0.271147 0.260374
0.191019 0.049935 0.033137
0.285752 0.052357 0.087905 0.135436 0.171836 0.231208 0.229619
0.172225 0.041982 0.0331681.194196 0.637309 0.265245 0.064225
0.061068 0.125048 0.13789 0.126597 0.049995 0.048814
3.726196 2.458763 1.371645 0.727895 0.399232 0.219581 0.076471
0.032355 0.057434 0.071706
2.650112 3.531786 3.194004 2.080157 1.421648 0.968323 0.436629
0.10932 0.065156 0.093496
2.038676 3.146064 3.963581 3.790196 3.11906 1.993903 0.950489
0.432626 0.193162 0.035039
1.52447 2.711524 3.462368 3.666123 3.532514 2.841281 1.579837
0.901566 0.324967 0.134061
Step 18.
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Since the average error is less than 45%, necessary steps
regarding the processing of image of test imagehas been made using
the technique of artificial neural network for the purpose of
recognition.
3.5 Noise removal/ Correction of Image using Artificial Neural
Network.
Step 19.
The Feed forward back propagation neural network has been used
on the test data matrix of the test imagefor training and testing
with reference to the original data matrix of the original image.
The configurationof Feed forward back propagation neural network is
as follows.
Feed-forward networks usually consist of three to four layers in
which the neurons are logically arranged.
The first and last layers are the input and output layers
respectively and there are usually one or morehidden layers in
between the other layers. Here information is only allowed to
"travel" in one direction.
This means that the output of one layer becomes the input of the
next layer, and so forward. In order forthis to occur, each layer
is fully connected to next layer and each neuron is connected by a
weight to a
neuron in the next layer.
Step 20.
After the application of artificial neural network as furnished
in step 16, a new modified data Matrixnamed MODMAT[][] has been
produced as furnished in table -6. It is to note that first 10X10
pixels are
stored in table 6 for better presentation.
Table -6 Modified Data Matrix MODMAT[][]
125 127 127 127 127 127 127 127 119 114
127 127 127 127 127 127 127 126 120 115
127 127 127 127 127 127 127 127 121 115
127 127 127 127 127 127 127 127 122 114
127 127 127 127 127 127 127 127 121 116
90 106 125 127 127 127 127 127 122 118
28 65 66 86 100 109 118 124 124 123
33 29 44 49 58 69 90 110 125 126
29 28 29 28 31 45 63 83 106 115
24 24 26 27 31 34 45 63 103 113
3.6 Calculation of estimated Error and Average Error.
Step 21.
The estimated error and average error of the values as stored in
table -6 with reference to the values storedin table -3 have been
calculated and the average error has been found as 14.39%. The
image based on
values as stored in table-6 has been formed which has been
furnished in figure-4.
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Step 22.
Other test images as furnished in figure- 5 have been taken for
processing and recognition.
4.
ResultsSerial No. Original
Image
Test
Image
Average
Error
Revised
Test Image
with ANN
Average
Error
Remarks
1 Figure -2 Figure -3 31% Figures -4 14.39% Recognition
Possible
2 Figure -5 Figure -6 74% Figures -4 64% Recognition
Not
Possible
Figure -2 Figure -3 Figures -4
Input Original Image Input Test Image Revised Image after
ANN
Figure -5 Figures -6
Input Original Image Input Test Image Remarks
5. ConclusionIt has been observed that if the average error is
less than 45%, Artificial Neural network can be applied
for training and testing for the purpose of recognition.
Therefore the test image is recognized and matchedsuccessfully with
original image. It has also been observed that, if the average
error is greater than 45%then the image is recognized as a
different image.
Processing Not Possible
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Reference.
[1] M. Egmont-Petersena, D. de Ridderb, H. Handelsc, L.
Beaurepaire, K.Chehdi; B.Vozel Image
processing with neural networksa review", Pattern Recognition 35
(2002), 2002, PP-22802288
[2] Berend Jan van der Zwaag , Kees Slump , and Lambert
Spaanenburg " Extracting Knowledge
from Neural Networks in Image Processing" PP-143-145
[3] Anil K. Jain, Fellow, IEEE, Robert P.W. Duin, and Jianchang
Mao, Senior Member, IEEE Statistical
Pattern Recognition: A Review, IEEE TRANSACTIONS ON PATTERN
ANALYSIS AND MACHINE
INTELLIGENCE VOL. 22, NO. 1, JANUARY 2000
[4] Andrzej Dziech, Ali Amuri, CONTOUR RECOGNITION USING NEURAL
NETWORKAPPLICATION,
Communications Dept., AGH Cracow University, Cracow, POLAND,
Electronics and Communication
Dept., Kielce University of Technology, Kielce, POLAND.
[5] Kenji Suzuki, Pixel-Based Artificial Neural Networks in
Computer-Aided Diagnosis, Department
of Radiology, Division of Biological Sciences, the University of
Chicago USA
[6] N.R. Pal, S.K. Pal, "A review on image segmentation
techniques", Pattern Recognition 26 (9)
(1993) 1277 1284.
[7] S.K. Pal, A. Ghosh, "Neuro-fuzzy computing for image
processing and pattern recognition", Int. J.
Systems Sci. 27 (12) (1996) 11821193
[9] Mostafa Jabarouti Moghaddam, Hamid Soltanian-Zadeh, Medical
Image Segmentation Using
Artificial Neural Networks .
[10] Adilson Gonzaga, Armando Marin, Evandro A. Silva, Fabiana
C. Bertoni Kelton A.P. Costa, Luciana
A.L. Albuquerque Neutral Facial Image Recognition Using Parallel
Hopfield Neural Networks,
Universidade de So Paulo
[10] Y.J. Zhang, "A survey on evaluation methods for image
segmentation", Pattern Recognition 29
(8) (1996) 1335 1340
[11] Humayun Karim Sulehria, Ye Zhang, Hopfield Neural NetworksA
Survey, Proceedings of the
6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge
Engineering and Data Bases, Corfu Island,
Greece, February 16-19, 2007.
