The area of character recognition has received a considerable attention by researchers all over the world during the last three decades. However, this research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic numeral using the Statistical styles in two methods and making comparison study between them. First method Linear Discriminant function that is yield results with accuracy as high as 90% of original grouped cases correctly classified. In the second method, we proposed algorithm, The results show the efficiency of the proposed algorithms, where it is found to achieve recognition accuracy of 92.9% and 91.4%. This is providing efficiency more than the first method.
In this paper, the discriminant analysis is used to classify the most wide spread heart diseases known as coronary heart diseases into two groups (patient, not patient) based on the changes of discrimination features of ten predictor variables that we believe they cause the disease . A random sample for each group is employed and the stepwise procedures are performed in order to delete those variables that are not important for separating the groups. Tests of significance of discriminant analysis and estimating the misclassification rates are performed
This paper presents an approach to license plate localization and recognition. A proposed method is designed to control the opening of door gate based on the recognition of the license plates number in Iraq. In general the system consists of four stages; Image capturing, License plate cropping, character segmentation and character recognition. In the first stage, the vehicle photo is taken from standard camera placed on the door gate with a specific distance from the front of vehicle to be processed by our system. Then, the detection method searches for the matching of the license plate in the image with a standard plate. The segmentation stage is performed by is using edge detection. Then character recognition, done by comparing with templ
... Show MoreFace Identification system is an active research area in these years. However, the accuracy and its dependency in real life systems are still questionable. Earlier research in face identification systems demonstrated that LBP based face recognition systems are preferred than others and give adequate accuracy. It is robust against illumination changes and considered as a high-speed algorithm. Performance metrics for such systems are calculated from time delay and accuracy. This paper introduces an improved face recognition system that is build using C++ programming language with the help of OpenCV library. Accuracy can be increased if a filter or combinations of filters are applied to the images. The accuracy increases from 95.5% (without ap
... Show MoreIris detection is considered as challenging image processing task. In this study efficient method was suggested to detect iris and recognition it. This method depending on seed filling algorithm and circular area detection, where the color image converted to gray image, and then the gray image is converted to binary image. The seed filling is applied of the binary image and the position of detected object binary region (ROI) is localized in term of it is center coordinates are radii (i.e., the inner and out radius). To find the localization efficiency of suggested method has been used the coefficient of variation (CV) for radius iris for evaluation. The test results indicated that is suggested method is good for the iris detection.
Many fuzzy clustering are based on within-cluster scatter with a compactness measure , but in this paper explaining new fuzzy clustering method which depend on within-cluster scatter with a compactness measure and between-cluster scatter with a separation measure called the fuzzy compactness and separation (FCS). The fuzzy linear discriminant analysis (FLDA) based on within-cluster scatter matrix and between-cluster scatter matrix . Then two fuzzy scattering matrices in the objective function assure the compactness between data elements and cluster centers .To test the optimal number of clusters using validation clustering method is discuss .After that an illustrate example are applied.
Face recognition system is the most widely used application in the field of security and especially in border control. This system may be exposed to direct or indirect attacks through the use of face morphing attacks (FMAs). Face morphing attacks is the process of producing a passport photo resulting from a mixture of two images, one of which is for an ordinary person and the other is a judicially required. In this case, a face recognition system may allow travel of persons not permitted to travel through face morphing image in a Machine-Readable Electronic Travel Document (eMRTD) or electronic passport at Automatic Border Control (ABC) gates. In creating an electronic passport, most countries rely on applicant to submit ima
... Show MoreA proposed feature extraction algorithm for handwriting Arabic words. The proposed method uses a 4 levels discrete wavelet transform (DWT) on binary image. sliding window on wavelet space and computes the stander derivation for each window. The extracted features were classified with multiple Support Vector Machine (SVM) classifiers. The proposed method simulated with a proposed data set from different writers. The experimental results of the simulation show 94.44% recognition rate.
Face detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method.
Image texture is an important part of many types of images, for example medical images. Texture Analysis is the technique that uses measurable features to categorize complex textures. The main goal is to extract discriminative features that are used in different pattern recognition applications and texture categorization. This paper investigates the extraction of most discriminative features for different texture images from the “Colored Brodatz” dataset using two types of image contrast measures, as well as using the statistical moments on five bands (red, green, blue, grey, and black). The Euclidean distance measure is used in the matching step to check the similarity degree. The proposed method was tested on 112 classes o
... Show Moreone of the most important consequences of climate change is the rise in sea levels, which leads to the drowning of some low-lying island states, which leads to them losing the elements of statehood and thus affecting their status as a state, this resulted in several proposals made by the jurisprudence of international law to solve this issue, perhaps the most important of which is the idea of the government in exile, and the proposal to continue recognition of submerged countries, in a way that makes it possible to talk about a new concept of states represented by deterritorialized states, all of which are ultimately proposals that contain great difficulties that hinder their implementation in reality.