Processing Using Artificial

strangerwineAI and Robotics

Oct 19, 2013 (3 years and 7 months ago)

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Image Recognition and
Processing Using Artificial
Neural Network

Md.
Iqbal

Quraishi
, J Pal
Choudhury

and

Mallika

De, IEEE

Outline


Introduction


Related work


Method


Experiments Result and Analysis


Conclusion

2

Introduction


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 of artificial neural network have
been spread to a wide range of domain
like
image recognition
,
fingerprint
recognition

and so on.

3

Related work(1/4)


The appearance of digital computers and
the development of
modern theories of
learning

and
neural processing

both
occurred at about the same time, during
the
late 1940s
.



To model individual neurons as well as
clusters of neurons, which are called
neural networks.

4

Related work(2/4)


A new approach for
feature extraction
based on the calculation of
eigen

values
from a contour was proposed and found
that using
feed forward neural network
satisfactory results were obtained.

5

Related work(3/4)

6


Feed Forward Neural Network








Related work(4/4)

7

Method


Processing of Original Image


The initial optimal image has been taken as
furnished in Fig
-
2 which has been considered
as original image.



8

Fig
-
2

Table
-
1

Input Data Matrix

Method


The average error after insertion of
salt
and pepper noise
has been calculated
which is 25.67%.


9

Table
-
2

Input Data Matrix with Noise

Fig
-
3

Method


Processing of Noisy Image


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 have
been calculated with reference to the values
stored in original data matrix. The a
verage
error

has been found as
5.397%
.

10

Method


The original image after removal of noise
has been transformed into data matrix
containing pixel values which have been
furnished in Table
-
3.


11

Table
-
3

Input Data Matrix after Noise
Removal

Fig
-
4

Method


For easier calculation
four pixels
have
been taken together.



The binary values of four pixels together
side by side have been combined and
formed as
32 bit binary number
.



Now the 32 bit binary number has been
converted into a decimal number.

12

Method


The decimal number as generated in
page
11

has been placed in original data matrix
termed as
ORMAT[][]
.


13

Table
-
4

Original Data Matrix ORMAT[][]

Method


The instructions furnished in
page 12
to
page 13
have been repeated for the total
pixel value of the original image after
noise removal as stored in Table
-
3.



Therefore a matrix has been produced
which has been stored in data matrix
termed as
ORMAT[][]
as furnished in
Table
-
4.

14

Method


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.

15

Fig
-
5

Table
-
5

Test Data Matrix

Method


Instructions as furnished in
page 9
have
been executed on test image to generate
test data matrix with noise as furnished in
Table
-
6.

16

Fig
-
6

Table
-
6

Test Data Matrix with Noise

Method


Instructions as furnished in
page 10
have
been executed on test image with noise
to generate test data matrix after noise
removal as furnished in Table
-
7.

17

Fig
-
7

Table
-
7

Test Data Matrix after Noise Removal

Method


Procedures as mentioned from
page 11
to
page 13
have been executed on test
image after noise removal to generate the
decimal number which has been placed in
test data matrix
TESTMAT[][]
.

18

Table
-
8

TESTMAT[][]

Method


Calculation of Average Error of test data
matrix based on original data matrix.


The estimated error and average error of the
values stored in decimal matrix as furnished in
Table
-
9
have been calculated with reference
to the values stored in original data matrix as
stored in
Table
-
4
. The
average error
has been
found as
31%
.

19

Method

20






Since the average error is
less than 45%
,
necessary steps regarding the processing
of test image has been made using the
technique of artificial neural network for
the purpose of recognition.

Table
-
9

Estimated Error Data

Method


Processing of Image towards recognition using
Artificial Neural Network.


The
feed forward back propagation neural
network

has been used on the test data
matrix of the test image for training and
testing with reference to the original data
matrix of the original image.


A new data matrix named NEWMAT[][] has
been produced as a result which has been
furnished in Table
-
10.

21

Method








It takes considerably
less time
to
complete the training and Testing using
ANN.

22

Table
-
10

Data Matrix NEWMAT[][] after ANN
application

Method


Each value of the data matrix
NEWMAT[][]
has been converted
into 32
bit

binary number.


Now the 32 bit binary number has been
divided into four 8 bit binary numbers.


Each 8 bit binary value has been
converted into decimal and each of them
has been considered as pixel values for
four consecutive pixels row wise.

23

Method


The instructions furnished in
page 23
have
been repeated for the total values of the
data matrix
NEWMAT[][].



As a result a new modified data Matrix
named
MODMAT[][]
has been produced
as furnished in Table
-
11.


24

Method

25

Table
-
11

Modified Data Matrix MODMAT[][]

Fig
-
8

Method

26


Calculation of estimated Error and Average
Error.


The estimated error and average error of the
values as stored in Table
-
11 with reference to
the values stored in Table
-
3 have been
calculated and the
average error
has been
found as
14.39%
.

Experiments Result and Analysis

27

Serial
Number

Original Image

Noisy Original

Image

Average Error

with

respect to Original Image


1


25.67%


2


26.42%

Experiments Result and Analysis

28

Serial
Number

Original Image
after

Noise Removal

Average Error

with

respect to

Original Image after Noise Removal


1


5.39%


2


2.93%

Experiments Result and Analysis

29

Serial
Number

Test Image

Noisy Test

Image

Average Error


due to Noise

with

respect to Test Image


1


25.75%


2


27.39%

Experiments Result and Analysis

30

Serial
Number

Test Image

After


Noise Removal

Average Error

with

respect to Test Image


1


5.56%


2


7.8%

Experiments Result and Analysis

31

Serial
Number

Average

Error
with


respect to
Original Image
after Noise
Removal

Test Image

after

training using

ANN

Average Error
with


respect to
Original Image

Remarks


1


31%


14.39%

Recognition

Possible


2


64%


---------


---------


Recognition
Not
Possible

Conclusion


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 matched successfully with original
image.

32

Conclusion


If the average error is
greater than 45%
then the image is recognized as a different
image.



It takes less time for training and testing
using ANN as number of rows of the
matrix used for training has
one fourth
number

of columns compare to the
original image.

33




Thank you for you listening

34