Morphological segmentation and classification of underground pipe images

Authors:
K. Sinha;W. Fieguth
Affiliations:
Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, 16801, PA, USA;Department of Systems Design Engineering, University of Waterloo, University Park, N2L 3G1, Waterloo, Ontario, Canada
Venue:
Machine Vision and Applications
Year:
2006

Citing 5
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to mathematical morphology

Computer Vision, Graphics, and Image Processing
Automatic thresholding of gray-level pictures using two-dimensional entropy

Computer Vision, Graphics, and Image Processing
Gray Level Thresholding in Badly Illuminated Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Split-and-merge image segmentation based on localized feature analysis and statistical tests

CVGIP: Graphical Models and Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

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