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dc.contributor.authorAwrangjeb, Mohammad
dc.contributor.authorJonas, David
dc.contributor.authorZhou, Jun
dc.contributor.editorGuo, Y
dc.contributor.editorLi, H
dc.contributor.editorCai, W
dc.contributor.editorMurshed, M
dc.contributor.editorWang, Z
dc.contributor.editorGao, J
dc.contributor.editorFeng, DD
dc.date.accessioned2018-07-02T01:30:24Z
dc.date.available2018-07-02T01:30:24Z
dc.date.issued2017
dc.identifier.isbn9781538628393
dc.identifier.doi10.1109/DICTA.2017.8227407
dc.identifier.urihttp://hdl.handle.net/10072/368376
dc.description.abstractThis paper proposes a new pylon detection technique from point cloud data. Two masks are created from the non-ground points that mainly represent trees and power line components. The first mask is the power line mask Mₘ, which contains the power line components and trees and where successive pylons are found connected with wires. The second mask is the pylon mask Mₚ, where successive pylons are found disconnected, and thus is exploited to obtain candidate pylons using a connected component analysis. By contrasting the area, shape and symmetry properties between trees and pylons majority of the false candidates (trees) are removed from Mₚ. Finally, long straight lines that represent wires between successive pylons are extracted from Mₘ and used to remove the remaining trees from Mₚ. Experimental results show that the proposed technique provides a high pylon detection rate in terms of both completeness (100%) and correctness (100%).
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameInternational Conference on Digital Image Computing - Techniques and Applications (DICTA)
dc.relation.ispartofconferencetitle2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
dc.relation.ispartofdatefrom2017-11-29
dc.relation.ispartofdateto2017-12-01
dc.relation.ispartoflocationSydney, AUSTRALIA
dc.relation.ispartofpagefrom532
dc.relation.ispartofpageto539
dc.relation.ispartofvolume2017-December
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPhotogrammetry and Remote Sensing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode090905
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0801
dc.titleAn Automatic Technique for Power Line Pylon Detection from Point Cloud Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun
gro.griffith.authorAwrangjeb, Mohammad


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