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Overview

               

In any manufacture process, quality control is very essential to ensure that the product is free from defect. Defect Identification, Isolation and Resolution are utmost important to ensure the accuracy of the results achieved by any manufacture process. By applying multi-level wavelet decomposition to the input image and then choosing a group of sub- bands to be restored for best defect detection, the technique used detects a defect in both structural and statistical texture. This makes the proposed technique computationally efficient because that it does not rely on the texture feature extraction on pixel by pixel basis. The experimental results showed that a few factors are considered to be very important for best defect detection: The type of wavelet bases which various wavelet bases can be used with superiority of compact support orthogonal wavelet bases. Also, 3 or 4 level can enhance the defect properly for a detection task. The image restoration strategy is important in which the different image restoration levels for different texture classes were used for defect detection in textures. The results on statistical texture showed that defect enhancement by restoring the coarse approximation of image is rotation invariant. Also, restoring some details of sub bands to enhance the defect in structural texture may cause unpredictable changes in image rotation. Beside manufacture inspection, this texture analysis can be tuned to inspect space station/shuttle damage or for cancer screening with mouth/colon/cervix ex-vivo scopes.