caa a22 4500 467936722 CHVBK 20180406152959.0 cr unu---uuuuu 170328e20060401xx s 000 0 eng 10.1007/s00138-005-0012-0 doi (NATIONALLICENCE)springer-10.1007/s00138-005-0012-0 Morphological segmentation and classification of underground pipe images [Elektronische Daten] [Sunil Sinha, Paul Fieguth] 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 Springer-Verlag, 2005 Segmentation nationallicence Morphology nationallicence Classification nationallicence Inspection nationallicence Sinha Sunil Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, 16801, PA, USA aut Fieguth Paul Department of Systems Design Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada aut Machine Vision and Applications Springer-Verlag 17/1(2006-04-01), 21-31 0932-8092 17:1<21 2006 17 138 https://doi.org/10.1007/s00138-005-0012-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00138-005-0012-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sinha Sunil Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, 16801, PA, USA aut NATIONALLICENCE 700 1- Fieguth Paul Department of Systems Design Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 17/1(2006-04-01), 21-31 0932-8092 17:1<21 2006 17 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer