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application of neural network

Pattern Recognition

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application of neural network

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  1. Pattern Recognition 1- Define of Pattern Recognition. 2- Examples of Pattern Recognition. 3- Components of Pattern Recognition. 4- Pattern Recognition Approaches or Methods. 5- Explain fingerprints.

  2. What is Pattern Recognition ? If we ask a question: How do you recognize the child in the image of the car as an example? Of the answer is because it knows that it is a car. From here we agree that any way to identify patterns that must be preceded by a learning phase of this patterns. # So stages to identify any pattern of two phases: 1- learning. 2- classification or recognition.

  3. Examples of applications • Optical Character Recognition (OCR) • Biometrics • Diagnostic systems • Military applications • Handwritten: sorting letters by postal code, input device for PDA‘s. • Printed texts: reading machines for blind people, digitalization of text documents. • Face recognition, verification, retrieval. • Finger prints recognition. • Speech recognition. • Medical diagnosis: X-Ray, EKG analysis. • Machine diagnostics, waster detection. • Automated Target Recognition (ATR). • Image segmentation and analysis (recognition from aerial or satelite photographs).

  4. Components of PR system Sensors and preprocessing Feature extraction Class assignment Classifier Pattern Learning algorithm Teacher • Sensors and preprocessing. • A feature extraction aims to create discriminative features good for classification. • A classifier. • A teacher provides information about hidden state -- supervised learning. • A learning algorithm sets PR from training examples.

  5. Pattern Recognition Approaches or Methods: 1.Template-Matching and Correlation Method.2. Statical Approach.3. Syntactic and Structural Approach.4. Neural Networks Approach.

  6. Template-Matching and Correlation Method Phase learning in this method is based on a set of stored templates, a template of each class in the computer as shown in graphic: In the classification stage compares input image (Input pattern) with templates of each class , The result was compared with class X is greater than the result compared with the Class Y, They are classified as Class X and so on. The drawing shows:

  7. How is the comparison? Stored input image within the form of a matrix and compared with the templates in the device pixel by pixel and gives a value for comparison. This method is an easy and very primitive, the only difficulty in this method is a good choice of templates for each type as well as to determine the comparison criteria, especially if the image is within carrying distortions!? For example, if we used this method to identify thief, you have to take each criminal several shots to be stored on your computer: for cats and one side view of each hand, shot forward, cats and a 45-degree angle of view from the camera. Just imagine the storage space needed for each of these templates!

  8. Statical Approach In this way, Each pattern is described by the set of features That it is possible to express the values ​​of real. In the learning phase: Each pattern feature vector , as shown in Photo:

  9. In the stage of recognition or discrimination or classification, this is usually done by dividing the image area to the regions segmented, each region compared with the class, as shown in Photo: For example, if we want to identify the image of an apple, what are the properties of the apple store at the stage of learning?! Is, for example: color, shape, rotation, lower region, the upper region .... Etc..As well as being recognized on the apple, is divided into parts each part we compare the features with the characteristics of the class stored and so on.Difficulty here is in choosing the set of properties for each category and the decision rules in pattern recognition.

  10. Syntactic and Structural Approach In this way we not only numeric values ​​to the characteristics of each type, but add the inter-relation Interrelationships or Interconnection of Features of these characteristics in each category and which gives us the necessary structural information to identify patterns! Recent studies in this area concluded that the most powerful way to identify the patterns is the way it combines Statistic pattern recognition approach with Syntactic pattern recognition method and the one called Syntactic-Semantic approach. In a learning stage in this way represents the pattern usually tree or graph or string of primitives and relations.

  11. Decision-making process at the stage of recognition or classification is a process of Syntax analysis or in other words, program parsing procedure. And the highest proportion compared to the result from comparing the input image with each tree (or graph or string depending on the representation adopted in the application) is stored determines which class the input image belongs! Take for example:? Image inserted there by the two circles (properties) if the distance between them is between 1 to 2 cm could be classified image as an image pair, taking into account other characteristics and their relationship with each other, either if the distance between them meters it is possible that classified as a car with lights, taking into account other features of course ... Thus This method is used to identify the missile, target recognition, as well as to character recognition, and others.

  12. Neural Networks Approach Neural networks knowledge-based care of itself by scientists for many years in order to reach a way like a human in a way to recognize patterns.Based on the use of parallel processing of data at one time, this treatment are processors or units, or nodes - and the kidneys in one sense - related to each other through links with weights, which was seized during the process of training the network. In the field of pattern recognition set of images interference patterns to the neural network, whereupon the neural network adjusts weights according to a mechanism specific and long processes:Then, in stage classification provides for the network pattern and build on the weights in the classification of this pattern:

  13. Fingerprints • Introduction • Features of fingerprints • The pattern recognition system • Why using neural network? • The goal of this method • Preprocessing system • Feature extraction and selection • Invariant recognition • Result

  14. Features of fingerprints Fingerprints are imprints formed by friction ridges of the skin in fingers and thumbs. • Their pattern are permanent and unchangeable on each finger during all the life. • They are individual (the probability that two fingerprints are alike is about 1 in 1.9x10^15) • They have long been used for identification

  15. The pattern recognition system • Image acquisition converting a scene into an array of numbers that can be manipulated by a computer. • Edge detection and thinning are parts of the preprocessing step which involves removing noise, enhancing the picture and, if necessary, segmenting the image into meaningful regions

  16. The pattern recognition system • Feature extraction in which the image is represented by a set of numerical “features” to remove redundancy from the data and reduce its dimension. • Classification where a class label is assigned to the image/object by examining its extracted features and comparing them with the class that the classifier has learned during its training stage. • The main focus of this method is on these two last parts

  17. Why using neural network? • Neural network enable solutions to be found to problems where algorithmic methods are too computationally intensive or do not exist. • The problem of feature extraction and classification seems to be a suitable application for neural nets. • They offer significant speed advantages over conventional techniques.

  18. The goal of this method • This proposed method is based on a data model for fingerprints that is structural rather than coordinate. • This structural data model is robust with respect to traslation, rotation and distortion.

  19. Preprocessing system The first phase of the work is to capture the fingerprints image and convert it to a digital representation of 512x512 by 256 gray levels. • Histogram equalization technique is used to increase the contrast if the illumination condition is poor. But we are only interested in binary information

  20. Preprocessing system Binarization is usually performed by using Laplacian edge detection operator • Local derivative operator such as “Roberts”, “Prewitt” or “Sobel” • Thresholding tecnique The binary image is further enhanced by a thinning algorithm which reduces the image ridges to a skeletal structure

  21. Preprocessing system • The thinning algorithm while deleting unwanted points should not: • Remove end points • Break connectedness • Cause excessive erosion of the region After obtaining the binary form of the fingerprint image, there may be some irregularities caused by skinfolds and contiguous ridges or spreading of ink due to finger pressure, and so on..

  22. Preprocessing system • To remedy this problem, smoothing is necessary and includes: • Filling holes • Deleting redundant points • Removing noisy points • Filling potential missing points

  23. Feature extraction and selection • Selection of good feature is a crucial step in the process since the next stage sees only these features and acts upon them. • 150 different minutiae type have been identified but in practice only ridge ending and ridge bifurcation are used.

  24. Feature extraction and selection • Good features are those satisfying two requirements: • Small intraclass invariance (i.e. slightly different shapes with similar general characteristics should have numerically close values) • Large interclass separation (i.e. features from different classes should be quite different numerically)

  25. Feature extraction and selection • A multilayer perceptron network of three layers is trained to detect the minutiae in the thinned part image of size 128x128 • The first layer has nine units associated with the components of the input vector • The hidden layer has five units • The output layer has one unit corresponding to the number of the classes The network is trained to output ‘1’ when the input window is centered on the feature to be located and it outputs ‘0’ if minutiae are not present

  26. Feature extraction and selection the network is trained by using the backpropagation learning technique and the weight change is updated according to

  27. Feature extraction and selection • The trained network is then used to analyze the complete image by raster scanning the fingerprint via window of size 3x3 • In order to prevent the falsely reported features and select “significant” minutiae, two more rules are added to the system to guarantee perfect ridge forks are detected while excluding all other features: • At those potential minutiae feature points we examine them by increasing the window size to 5x5 • If two or more minutiae are too close togheter, we ignore all of them

  28. Feature extraction and selection • Distribution of minutiae of two identical fingerprints 2(a) before and 2(b) after applying the rules

  29. Invariant recognition • The location of a reference point of the fingerprints is important for invariant recognition and has to be determined • Contour tracing is used to find one or more turning points (i.e. points with maximum rate of change of tracing movement) • This points are then used to find the reference point

  30. Invariant recognition • The Euclidean distanced(i) from each feature point i to the reference point are calculated • The distance to the center confers the property of positional invariance • The data are then sorted in ascending order from d(0) to d(N) • this operation gives the data the property of rotational invariance • In order to make the data becomes invariant to scale change, it is normalized to unity by the shortest distance d(0), i.e. dist(i) = d(0)/d(i), i = 0..N • This will weight those feature points nearer to the center more heavly because usually these points are more significant in classification.

  31. Invariant recognition • The centroidal data patterns should be shift, scale and rotational independent. • Also the invariant feature vectors are in the range [0,1] and they can be directly used as the training/stored vectors in the MLP classifier.

  32. Result • The recognition rate of fingerprints depends much on the quality of the fingerprints and effectiveness of the preprocessing system • Such as the thresholding level used in edge detection • If there are too many broken lines or noisy points in the image, the preprocessing system contour tracing may fail. • An intelligent connection algorithm to recover broken lines and suppress spurious irregularities is necessary

  33. # أسماء المجموعة : 1- محمود محمد عبدالله أحمد . 2- عبدالرازق حسانين حسن . 3- محمد عبدالجليل حسان . 4- هناء حمدي . 5- شيماء عبدالحميد . 6- يمين مسعد .

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