Image Recognition and Processing Using Artificial Neural NetworkMd. Iqbal Quraishi, J Pal Choudhury and Mallika De, IEEE

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OutlineIntroductionRelated workMethodExperiments Result and AnalysisConclusion

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IntroductionArtificial 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.

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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.

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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.

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Related work(3/4)Feed Forward Neural Network

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Related work(4/4)

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MethodProcessing of Original Image

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

Fig-2 Table-1 Input Data Matrix

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MethodThe average error after insertion

of salt and pepper noise has been calculated which is 25.67%.

Table-2 Input Data Matrix with NoiseFig-3

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MethodProcessing 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 average error has been found as 5.397%.

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MethodThe original image after removal

of noise has been transformed into data matrix containing pixel values which have been furnished in Table -3.

Table-3 Input Data Matrix after Noise Removal

Fig-4

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MethodFor 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.

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MethodThe decimal number as

generated in page 11 has been placed in original data matrix termed as ORMAT[][].

Table-4 Original Data Matrix ORMAT[][]

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MethodThe 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.

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MethodProcessing 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.

Fig-5 Table-5 Test Data Matrix

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MethodInstructions as furnished in page

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

Fig-6 Table-6 Test Data Matrix with Noise

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MethodInstructions 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.

Fig-7 Table-7 Test Data Matrix after Noise Removal

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MethodProcedures 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[][].

Table-8 TESTMAT[][]

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MethodCalculation 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%.

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Method

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

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MethodProcessing 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.

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Method

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

Table-10 Data Matrix NEWMAT[][] after ANN application

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MethodEach 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.

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MethodThe 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.

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Method

Table-11 Modified Data Matrix MODMAT[][]

Fig-8

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MethodCalculation 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%.

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Experiments Result and Analysis

Serial Number

Original Image

Noisy Original Image

Average Error with

respect to Original Image

1 25.67%

2 26.42%

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Experiments Result and Analysis

Serial Number

Original Image after Noise

Removal

Average Error with

respect to Original Image after Noise Removal

1 5.39%

2 2.93%

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Experiments Result and Analysis

Serial Number

Test Image Noisy Test Image

Average Error due to Noise

with respect to Test Image

1 25.75%

2 27.39%

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Experiments Result and Analysis

Serial Number

Test Image After

Noise Removal

Average Error with

respect to Test Image

1 5.56%

2 7.8%

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Experiments Result and AnalysisSerial Numb

er

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

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ConclusionIf 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.

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ConclusionIf 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.

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