@article {387,
	title = {Morphological segmentation and classification of underground pipe images},
	journal = {Machine Vision and Applications},
	volume = {17},
	year = {2006},
	pages = {21 - 31},
	abstract = {<p>Visual inspection based on closed circuit television surveys is used widely in North America to assess the condition of underground pipes. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousand of miles of pipeline because of fatigue, subjectivity, and cost. In this paper, simple, robust, and efficient image segmentation and classification algorithm for the automated analysis of scanned underground pipe images is presented. The experimental results demonstrate that the proposed algorithm can precisely segment and classify pipe cracks, holes, laterals, joints and collapse surface from underground pipe images</p>
},
	keywords = {Classification, Inspection, Morphology, Segmentation},
	issn = {0932-8092},
	doi = {http://dx.doi.org/10.1007/s00138-005-0012-0},
	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 = {<p>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</p>
},
	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}
}
