الفهرس 
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Abstract
Automated visual inspection is becoming an important field of computer vision in many industries. It is used to determine the quality control for many industrial product such as spot and wrinkles in textile fabric surface, ceramic tile, cracks in steel bars surface, wooden products and scratching on glass surfaces. The real-time inspection of flat surface products is a task full of challenges in industrial aspects that require fast and accurate algorithms for detection and localization of defects. Structural, statistical, and filter-based approaches, such as Gabor Filter Banks, Log-Gabor filter, and Wavelets, have high computational complexity. This thesis introduces a fast and accurate model for inspection and localization of industrial flat surface products: Neighborhood Preserving Perceptual Fidelity Aware Mean Squared Error (NP-PAMSE). The Extreme Learning Machine (ELM) is used for classification. ELM is found to be the perfect classifier for detecting defects. The proposed model resulted in defect detection \accuracy of 99.86%, with 98.16% sensitivity, and 99.90% specificity. These results show that the proposed model outperforms many existing defect detection approaches. The discriminant power displays the efficiency of ELM in differentiation between normal and abnormal surfaces.