@article {391, title = {Segmentation of buried concrete pipe images}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {47 - 57}, abstract = {

The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America.

}, keywords = {Automated inspection, image processing, Mathematical morphology, Pipeline assessment, Pipeline Infrastructure, Segmentation}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.007}, author = {S K. Sinha and P Fieguth} } @inproceedings {534, title = {Morphological skeleton algorithm for PDP production line inspection}, booktitle = {IEEE Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Morphological skeletonization is an image processing technique that reduces complex, thick-lined images to a series of single pixel lines that accurately represent the original shapes. This procedure is especially useful to simplify automated applications requiring simple shape analysis and continuity checking by reducing the amount of redundant image data. In the semiconductor inspection field, skeletonization is a process that can be used to detect defects during plasma display panel (PDP) inspection. This paper introduces a novel morphological skeletonization algorithm developed for electrode pattern inspection of PDPs. This algorithm has been successfully integrated within a commercial machine vision system

}, keywords = {automated applications, commercial machine vision system, Computer Vision, continuity checking, defects detection, electrode pattern inspection, Feature Extraction, feature point matching, image matching, image processing, image representation, image thinning, Inspection, Mathematical morphology, morphological skeleton algorithm, PDP inspection, PDP production line inspection, plasma display panel, plasma displays, production, redundant image data reduction, semiconductor device testing, semiconductor inspection, shape analysis, shape representation, single pixel lines, thick-lined images}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933598}, 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} }