@inproceedings {527, title = {An automated and rapid defect inspection algorithm for fluorescent PDP patterns}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Plasma display panels (PDPs) represent the next generation of visual displays in the new century. Relative to traditional CRTs, PDPs offer advantages such as providing clearer images and occupying less space. The quality control of the PDP in the production line is very important to minimize costs and maximize product quality. Although there are several different kinds of patterns that need to be inspected, this research presents algorithms used for fluorescent pattern inspection of PDPs. Here, a complete design and implementation of a sequence of algorithmic components necessary to identify fluorescent pattern PDP defects is described. A primary criterion is that the maximum time allowed for the entire analysis is only a few seconds, so each component must execute rapidly and efficiently
Plasma display panels (PDPs) represent the next generation of visual displays in the new century. Relative to traditional CRTs, PDPs offer advantages such as providing clearer images and occupying less space. The quality control of the PDP in the production line is very important to minimize costs and maximize product quality. Although there are several different kinds of patterns that need to be inspected, this research presents algorithms used for fluorescent pattern inspection of PDPs. Here, a complete design and implementation of a sequence of algorithmic components necessary to identify fluorescent pattern PDP defects is described. A primary criterion is that the maximum time allowed for the entire analysis is only a few seconds, so each component must execute rapidly and efficiently
}, keywords = {automated rapid defect inspection algorithm, automatic optical inspection, costs, flat panel displays, fluorescence, fluorescent pattern inspection, fluorescent PDP patterns, image processing, plasma display panels, plasma displays, product quality, production line, quality control, visual displays}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933685}, author = {R Ge and D A. Clausi} } @inproceedings {516, title = {Underground pipe cracks classification using image analysis and neuro-fuzzy algorithm}, booktitle = {IEEE Symposium on Intelligent Control}, year = {1999}, abstract = {

Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection systems using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs. A recognition and classification method for pipe cracks using image analysis and a neuro-fuzzy algorithm is proposed. In the pre-processing step, the cracks in the pipe are extracted from the homogenous background. Then, based on prior knowledge of cracks, five normalised features are extracted. In the classification step, a neuro-fuzzy algorithm is proposed that employs a trapezoidal fuzzy membership function and modified error backpropagation algorithm

}, keywords = {asset managers, automatic inspection systems, automatic optical inspection, backpropagation, civil engineering, eigenvalues and eigenfunctions, Feature Extraction, fuzzy neural nets, homogenous background, Hough transforms, image analysis, image classification, Mathematical morphology, modified error backpropagation algorithm, neuro-fuzzy algorithm, statistical analysis, surface defects, trapezoidal fuzzy membership function, underground pipe cracks classification}, doi = {http://dx.doi.org/10.1109/ISIC.1999.796688}, author = {S K. Sinha and F Karray and P Fieguth} }