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dc.contributor.authorWard, Ben
dc.contributor.authorBastian, John
dc.contributor.authorvan den Hengel, Anton
dc.contributor.authorPooley, Daniel
dc.contributor.authorBari, Rajendra
dc.contributor.authorBerger, Bettina
dc.contributor.authorTester, Mark A.
dc.date.accessioned2015-06-03T08:37:04Z
dc.date.available2015-06-03T08:37:04Z
dc.date.issued2015-03-19
dc.identifier.doi10.1007/978-3-319-16220-1_16
dc.identifier.urihttp://hdl.handle.net/10754/556196
dc.description.abstractWe present a method for recovering the structure of a plant directly from a small set of widely-spaced images for automated analysis of phenotype. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is composed of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, without manual intervention.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-319-16220-1_16
dc.relation.urlhttp://arxiv.org/abs/1503.03191
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16220-1_16
dc.subjectPlant phenotyping
dc.subjectImage processing
dc.subjectPlant architecture
dc.titleA Model-Based Approach to Recovering the Structure of a Plant from Images
dc.typeConference Paper
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentDesert Agriculture Initiative
dc.contributor.departmentPlant Science
dc.contributor.departmentPlant Science Program
dc.identifier.journalComputer Vision - ECCV 2014 Workshops
dc.conference.date2014-09-06 to 2014-09-12
dc.conference.name13th European Conference on Computer Vision, ECCV 2014
dc.conference.locationZurich, CHE
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science, The University of Adelaide, Adelaide, Australia
dc.contributor.institutionBayer CropScience, Ghent, Belgium
dc.contributor.institutionThe Plant Accelerator, The University of Adelaide, Adelaide, Australia
dc.identifier.arxividarXiv:1503.03191
kaust.personTester, Mark A.
refterms.dateFOA2016-03-19T00:00:00Z
dc.date.published-online2015-03-19
dc.date.published-print2015
dc.date.posted2015-03-11


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