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dc.contributor.authorPeebles, Matthewen_NZ
dc.contributor.authorLim, Shen Hinen_NZ
dc.contributor.authorStreeter, Leeen_NZ
dc.contributor.authorDuke, Mikeen_NZ
dc.contributor.authorAu, Chi Kiten_NZ
dc.coverage.spatialAuckland, NEW ZEALANDen_NZ
dc.date.accessioned2019-09-17T02:52:57Z
dc.date.available2018-01-01en_NZ
dc.date.available2019-09-17T02:52:57Z
dc.date.issued2018en_NZ
dc.identifier.citationPeebles, M., Lim, S. H., Streeter, L., Duke, M., & Au, C. K. (2018). Ground Plane Segmentation of Time-of-Flight Images for Asparagus Harvesting. In 2018 International Conference on Image And Vision Computing New Zealand (IVCNZ). Auckland, NEW ZEALAND: IEEE.en
dc.identifier.issn2151-2191en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12878
dc.description.abstractA key task in processing time-of-flight images for the detection of asparagus spears is the identification and segmentation of the ground plane. In this paper, we aim to compare the performance of RANSAC with a modified version of an existing deterministic method, namely Hyun’s method. Each method is tested on scenes with varying amounts of field clutter. Additionally, a variety of camera angles are investigated. We find that both RANSAC and the proposed method produce ground plane predictions with a root mean square error of less than 0.05m and execute at a rate of approximately 0.056s per frame. However, RANSAC was shown to be much less reliable in high clutter scenes. The camera mounting angle is found to significantly affect the density and noise of points in time-of-flight images. These factors translate to significantly worse performance for both methods at low camera angles.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rightsThis is an author’s accepted version. © 2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceInternational Conference on Image and Vision Computing New Zealand (IVCNZ)en_NZ
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectComputer Science, Artificial Intelligenceen_NZ
dc.subjectImaging Science & Photographic Technologyen_NZ
dc.subjectComputer Scienceen_NZ
dc.subjectasparagusen_NZ
dc.subjectasparagus harvestingen_NZ
dc.subjectroboticsen_NZ
dc.subjectrobotic harvestingen_NZ
dc.subjectagricultural automationen_NZ
dc.subjectpoint cloudsen_NZ
dc.subjectground plane segmentationen_NZ
dc.titleGround Plane Segmentation of Time-of-Flight Images for Asparagus Harvestingen_NZ
dc.typeConference Contribution
dc.relation.isPartOf2018 International Conference on Image And Vision Computing New Zealand (IVCNZ)en_NZ
pubs.elements-id235777
pubs.finish-date2018-11-21en_NZ
pubs.publication-statusPublisheden_NZ
pubs.start-date2018-11-19en_NZ


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