No abstract

<p>The aim of this research focuses on construct a computerized system for textile defects detection. The system merges between image processing methods, statistical methods in addition to the Intelligent techniques via Neural Network and Fuzzy Logic. Gabor filters were used to identify edges and to highlight defective areas in fabric images, then to train the neural network on statistical and geometry features derived from fabric images to form the special neural network distinguish and classify defects into the fourteen categories, which are the most common defects in the textile factory.  The proposed work includes two phases. The first phase is to detect the defects in fabrics. The second phase is the classification phase of the defect. At the defect detection stage, a Discrete Cosine Transfer (DCT) converts the images to the frequency domain.  Image features then drawn and introduce them to the Elman Neural Network to detect the existence of defects. In the classification stage, the images are converted to the frequency domain by the Gabor filter and then the image features are extracted and inserted into the back propagation network to classify the fabric defects in those images. Fuzzy logic is then applied to neural network outputs and interference values are used in fuzzy logic to increase final discrimination. We evaluate a distinction rate of 91.4286% .After applying the fuzzy logic to neural network output; the discrimination rate was raised to 97.1428%. </p>

Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to the chromosome that enters the system. In the second method, we have classified the signature by calculating the Euclidean Distance between the query signature and the signatures stored in the database. The signature closest to a confirmed threshold is considered the desired goal. The database uses 25 handwritten signatures (15 signatures for training and five original signatures, and five fake signatures written by other people for testing), so we have a database of 500 signatures. With a 94% discrimination rate, the genetic recognition system (GRS) was able to access the solutions, and with a (91% rate) the euclidean recognition system (ERS) was done. The application uses MATLAB.

The automatic control of the fabric is one of the important steps in the spinning and weaving industry in order to preserve the quality of the fabric. The manual methods have been used for decades to control the product using human vision. The monitoring process is very strenuous, time consuming and cost effective. To reduce the costs required there arise the needing of automated systems appearance to examine, detect and apply tissue defects. The aim of the proposed work is to build an efficient system for detecting and classifying textile defects using advanced image processing techniques based on new methods of combining the practical implementation of image segmentation and features extraction, as well as the use of artificial intelligence techniques of neural networks for detection and classification. The system was built in two phases: the first is the defect detection phase, and the second phase is the classification phase, where live images were collected as a textile database from the textile factory in Mosul as well as the local market. The fabrics were carefully selected and these fabrics are of different types and colors, some of these have no defect at all and some of them have up to fourteen types of defects. 560 images were collected; 280 of which were non defective fabrics, 280 were defective, and there are 20 images for every type of defect, at the defect detection phase, the statistical second-class attributes of the GLCM matrix (energy, variance, correlation, homogeneity) are extracted, while in the classification phase, the statistical first-class attributes, mean and skewness, and the geometric attribute of the total defect size. Two neural networks were used as determinants of detection and classification: the Back Propagation Neural Network (BPNN) and the Elman network. The proposed system showed a 95.7% discrimination rate compared with other similar work in the same field.