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-2Table-1Input Data Matrix

Method The average error after insertion of salt and pepper noise has been calculated which is 25.67%. 9 Table-2Input Data Matrix with NoiseFig-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 average 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 Table-3Input 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-4Original 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-5Table-5Test 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 Fig-6Table-6Test 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 Fig-7Table-7Test 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-8TESTMAT[][]

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-9Estimated 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

Method It takes considerably less time to complete the training and Testing using ANN. 22 Table-10Data 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

Method 25 Table-11Modified 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 ImageNoisy Original Image Average Error with respect to Original Image % %

Experiments Result and Analysis 28 Serial Number Original Image after Noise Removal Average Error with respect to Original Image after Noise Removal 15.39% 22.93%

Experiments Result and Analysis 29 Serial Number Test ImageNoisy Test ImageAverage Error due to Noise with respect to Test Image % %

Experiments Result and Analysis 30 Serial Number Test Image After Noise Removal Average Error with respect to Test Image 15.56% 27.8%

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 131%14.39% Recognition Possible 264% 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