Paper Details

[ Downloads: 3 | Abstract Viewed: 439 | Pages: 5 ]

Abstract of the Article

Note: English CIVILICA is in its Trial Period so Full Texts can not be provided! Persian users can download it here

Automatic Segmentation and Classification of Pipeline Images Using Mathematic Morphology and Fuzzy K-Means Algorithm

Authors:

[ M. Ziashahabi ] - Department of Electrical Engineering, Shahed University, Tehran, Iran
[ H. Sadjedi ] - Department of Electrical Engineering, Shahed University, Tehran, Iran
[ H Khezripour ] - Department of Computer Engineering , Amir-Kabir Univ, Tehran

Abstract:

Defects on the Pipeline surface such as cracks cause main problems for governments, specifically when the pipeline is covered under the ground. Manual examination for surface defects in the pipeline has several disadvantages, including varying standards, and high cost. In this paper, a combination of two algorithms based on mathematical morphology and curvature evaluation for segmentation of defects is proposed. Then, we use fuzzy k-means clustering to classify pipe defects. The proposed method can be completely automated and has been tested on more than 250 scanned images of petroleum pipelines of Iran

Keywords:

Image processing, mathematical morphology,pipeline inspection, segmentation, classification