A fuzzy system for detection and classification of textile defects to ensure the quality of fabric production

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%.

A hyprid technique for human footprint recognition

Biometrics has concerned a great care recently due to its important in the life that starts from civil applications to security and recently terrorism. A Footprint recognition is one of the personal identifications based on biometric measurements.  The aim of this research is to design a proper and reliable biometric system for human footprint recognition named (FRBS) that stands for Footprint Recognition Biometric System. In addition, to construct a human footprint database which it is very helpful for various use in scientific application e.g. for authentication. There exist many biometrics databases for other identity but very rare for footprint. As well as the existing one are very limited. This paper presents a robust hyprid techniques which merges between Image Processing with Artificial Intelligent technique via Ant Colony Optimization (ACO) to recognize human footprint.  (ACO) plays the essential role that rise the performance and the quality of the results in the biometric system via feature selection. The set of the selected features was treated as exploratory information, and selects the optimum feature set in standings of feature set size. Life RGB footprint images from nine persons with ten images per person constructed from life visual dataset. At first, the visual dataset was pre-processed operations. Each resultant image detects footprint that is cropped to portions represented by three blocks. The first block is for fingers, the second block refers to the center of the foot and the last one determines the heel. Then features were extracted from each image and stored in Excel file to be entered to Ant Colony Optimization Algorithm. The experimental outcomes of the system show that the proposed algorithm evaluates optimal results with smaller feature set comparing with other algorithms. Experimental outcomes show that our algorithm obtains an efficient and accurate result about 100% accuracy in comparison with other researches on the same field