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3D plant modelling via hyperspectral imaging

Liang, Jie; Zia, Ali; Zhou, Jun; Sirault, Xavier R. R.

Description

Plant phenomics research requires different types of sensors be employed to measure the physical traits of plant surface and to estimate the plant biomass. Of particular interest is the hyperspectral imaging device which captures wavelength indexed band images that characterise material properties of objects under study. In this paper, we introduce a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. We show that...[Show more]

dc.contributor.authorLiang, Jie
dc.contributor.authorZia, Ali
dc.contributor.authorZhou, Jun
dc.contributor.authorSirault, Xavier R. R.
dc.coverage.spatialSydney Australia
dc.date.accessioned2015-12-10T23:07:45Z
dc.date.createdDecember 1-8 2013
dc.identifier.isbn9781479930227
dc.identifier.urihttp://hdl.handle.net/1885/63000
dc.description.abstractPlant phenomics research requires different types of sensors be employed to measure the physical traits of plant surface and to estimate the plant biomass. Of particular interest is the hyperspectral imaging device which captures wavelength indexed band images that characterise material properties of objects under study. In this paper, we introduce a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. We show that hyperspectral imaging has shown clear advantages in segmenting plant from its background and is promising in generating comprehensive 3D plant models.
dc.publisherIEEE
dc.relation.ispartofseries2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013
dc.sourceProceedings of the IEEE International Conference on Computer Vision
dc.subjectKeywords: Spectroscopy; 3D plant modeling; Hyper-spectral images; Hyperspectral Imaging; Plant biomass; Plant model; Plant modelling; Plant surfaces; Proof of concept; Three dimensional
dc.title3D plant modelling via hyperspectral imaging
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2013
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationU3488905xPUB759
local.type.statusPublished Version
local.contributor.affiliationLiang, Jie, College of Engineering and Computer Science, ANU
local.contributor.affiliationZia, Ali, Griffith University
local.contributor.affiliationZhou, Jun, Griffith University
local.contributor.affiliationSirault, Xavier R.R, CSIRO Plant Industry
local.description.embargo2037-12-31
local.bibliographicCitation.startpage172
local.bibliographicCitation.lastpage177
local.identifier.doi10.1109/ICCVW.2013.29
dc.date.updated2022-02-06T07:18:37Z
local.identifier.scopusID2-s2.0-84897482159
local.identifier.thomsonID000349847200024
CollectionsANU Research Publications

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